CARGA DE LIBRERIAS

library(gplots)
library(foreign)
library(car)
library(plotly)
library(ggpubr)
library(ggplot2)
library(reprex)
library(dplyr)
library(readxl)
library(readr)
library(rpart)
library(glmulti)
library(caret)
library(InformationValue)
library(rvest)
library(stringi)
library(stringr)
library(dplyr)
library(fmsb)
library(dlookr)
library(ggrepel)
library(rattle)
library(corrplot)
library(factoextra)
library(mclust)
library(FactoMineR)
library(tidyr)
library(GGally)
library(gridExtra)
library(grid)
library(FSelector)
library(mlbench)
library(RRF)
library(wsrf)
library(Boruta)
options(warn=-1)

options(repr.plot.width = 18, repr.plot.height = 10)

IMPORTACION Y LECTURA DE LOS DATOS

options(repr.matrix.max.rows=600, repr.matrix.max.cols=200)

datos <- as.data.frame(read_excel("C:/Users/34625/Downloads/PROYECTOS DE TRABAJO/Yankel Carolina Sena/fwddatayankelsena/original_yankel.xlsx",sheet='Hoja3'))
datos

A data.frame: 59 × 34

STATUS	CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	WBC_PRE	RBC_PRE	HB_PRE	PLT_PRE	CRP_PRE	ALBUMIN_PRE	LDH_PRE	eGFR_PRE	AST_PRE	ALT_PRE	K_PRE	BGL_PRE_PET1	BMI	BW_PRE	SPLEEN_FDG_UPT_PRE_PET1	BM_UPT_PRE_PET1	LIVER_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	SLR_pre	BMLR_pre	OVERALL_TIME
<chr>	<dbl>	<chr>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
ALIVE	3	MALE	54	III	3.82	3.03	9.70	146	3.67	2.13	573.0	60.1	16.80	6.70	4.87	93	22.90	66.50	2.67	2.86	2.45	316.5	GINECOLOGICAL	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	25	0.8586957	1.0144928	2
ALIVE	2	FEMALE	79	IV	10.87	3.93	11.50	515	4.11	2.65	158.0	57.3	13.50	8.00	3.92	108	16.47	33.50	2.40	2.70	2.70	102.3	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DRUG INTERRUPTED	4	0	1.0099502	0.9303483	50
ALIVE	3	MALE	60	III	8.40	4.41	8.56	355	0.09	4.32	77.8	82.1	19.10	13.70	4.56	87	20.15	58.10	2.57	1.31	2.27	81.5	LUNG CANCER	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE REDUCED	4	14	0.9955752	1.0884956	2
ALIVE	3	MALE	76	II	4.66	3.55	11.70	178	0.10	3.70	195.0	65.7	17.70	15.30	3.83	135	22.83	56.70	2.25	2.46	2.26	224.4	RENAL CANCER	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	0.8811475	0.7172131	12
ALIVE	5	FEMALE	70	II	5.86	5.19	15.10	343	7.26	3.94	166.0	79.9	18.10	10.20	3.83	111	18.29	41.70	2.48	2.52	2.55	260.0	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	0.8269231	1.0192308	12
ALIVE	4	MALE	54	IV	2.95	4.45	14.50	167	0.25	4.16	135.0	76.3	14.40	14.00	4.55	98	27.18	80.40	2.01	1.40	2.83	198.4	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	HIGHT SEVERE	DOSE NOT CHANGED	6	363	1.1321586	0.5770925	18
ALIVE	2	MALE	60	III	4.25	4.82	15.70	220	0.03	4.47	201.0	75.4	25.30	30.00	4.55	101	22.76	72.10	2.37	2.80	2.76	257.3	LUNG CANCER	ICI	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	9	0	1.1615721	1.1877729	7
ALIVE	5	MALE	69	III	9.17	2.25	7.30	581	4.70	2.73	483.0	89.2	25.60	40.60	3.85	162	17.36	44.10	1.98	2.20	1.49	104.1	HEAD AND NECK	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE NOT CHANGED	9	95	0.7482270	0.8368794	16
ALIVE	6	MALE	64	IV	9.80	4.36	13.90	357	0.60	4.38	139.0	54.4	21.20	27.90	5.15	112	23.65	63.60	2.66	2.72	2.29	122.9	HEAD AND NECK	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SLIGTHLY SIDE EFFCTS	DOSE NOT CHANGED	10	28	0.7102473	0.4946996	65
ALIVE	12	MALE	72	III	5.61	3.66	11.60	282	0.10	3.73	198.0	57.5	20.30	20.30	4.69	128	22.84	68.60	2.03	1.87	2.01	142.5	HEAD AND NECK	ICI	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	16	0	1.0000000	0.9090909	1
ALIVE	29	MALE	71	III	4.51	4.41	13.50	176	0.30	3.97	175.0	51.9	20.30	14.50	4.29	100	21.34	58.10	2.19	1.97	2.83	166.4	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	ADDED OTHER TREATMEN	21	0	0.8007968	1.0717131	41
ALIVE	6	MALE	67	III	7.49	3.61	10.60	153	0.44	3.83	184.0	81.4	13.60	14.60	4.09	100	20.29	56.60	2.20	2.00	2.20	122.3	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	25	0	1.1373391	0.9871245	11
ALIVE	48	MALE	60	III	8.38	4.12	11.00	239	0.19	3.38	130.0	76.5	15.30	16.30	3.92	100	29.11	67.70	2.01	2.69	2.51	183.7	RENAL CANCER	ICI	ASYMTOMATIC	HYPERLIPIDEMIA	SLIGTHLY SIDE EFFCTS	UNKNOW	25	735	0.7814815	1.1444444	11
ALIVE	20	MALE	61	IV	6.11	4.10	12.10	495	1.28	2.91	125.0	67.7	20.10	20.00	4.58	85	20.12	54.90	2.05	2.90	1.53	203.1	MELANOMA	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	NO SIDE EFFECTS	UNKNOW	38	0	0.7867133	0.7657343	15
DEATH	2	MALE	83	II	3.45	3.98	11.60	198	0.20	3.73	214.0	46.9	13.70	9.00	4.81	84	24.46	62.00	2.44	2.06	2.67	56.9	MELANOMA	ICI	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	11	0	0.7645503	0.8756614	4
DEATH	3	MALE	68	I	5.14	4.22	11.00	268	0.42	3.75	253.0	65.7	27.80	16.20	3.92	100	24.41	75.80	2.15	2.65	2.60	92.5	MELANOMA	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	2	0	0.8062500	0.7062500	13
DEATH	18	MALE	77	II	4.69	3.34	9.60	183	0.06	3.43	125.0	39.8	11.90	3.20	3.98	104	18.07	51.80	2.15	1.75	2.44	262.6	RENAL CANCER	ICI	ASYMTOMATIC	CHRONIC INFLAMMATION	SEVERE	ADDED STEROIDS	10	3	1.0000000	0.9090909	12
DEATH	4	MALE	60	III	8.66	4.80	15.00	247	1.29	3.81	139.0	57.6	13.00	12.70	3.90	116	23.14	64.30	2.74	3.04	3.41	160.0	LUNG CANCER	ICI	SYMPTOMATIC BUT AMBULATORY	CHRONIC INFLAMMATION	MODERATE	UNKNOW	15	441	0.9423077	1.1153846	29
DEATH	7	MALE	64	IV	3.46	3.85	14.60	140	0.23	4.10	172.0	51.9	18.60	11.70	3.75	96	22.12	65.90	2.11	2.36	2.82	174.7	HEAD AND NECK	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DRUG INTERRUPTED	4	52	0.7806691	0.9665428	42
DEATH	15	MALE	67	II	13.32	3.70	11.30	442	18.63	2.18	670.0	96.6	14.90	23.10	4.51	95	14.08	14.08	2.53	1.88	1.89	117.8	HEAD AND NECK	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	SLIGTHLY SIDE EFFCTS	DRUG INTERRUPTED	13	364	1.2717949	1.1282051	21
DEATH	5	MALE	83	IV	4.50	3.86	13.10	158	0.36	4.19	111.0	114.0	13.00	9.50	4.17	100	20.07	47.30	2.15	2.51	2.08	281.0	HEAD AND NECK	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	8	0	0.9881890	0.9094488	21
DEATH	4	FEMALE	56	II	3.75	4.03	12.00	143	3.87	4.47	751.0	45.7	252.20	281.50	4.71	104	21.79	60.40	2.29	2.41	2.98	143.0	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	HIGHT SEVERE	DOSE REDUCED	9	233	1.1592040	0.9552239	8
ALIVE	7	MALE	65	II	7.10	4.58	10.50	269	0.35	3.22	159.0	51.7	27.50	26.50	4.65	90	18.18	50.10	2.40	2.83	2.86	114.2	PANCREAS CANCER	CHEMO	ASYMTOMATIC	DIABETES MELLITUS	NO SIDE EFFECTS	UNKNOW	1	0	0.8932039	0.8543689	27
ALIVE	5	FEMALE	55	IV	7.06	5.03	10.20	260	0.06	4.29	214.0	66.0	15.30	29.80	3.97	77	25.60	62.30	2.26	2.30	2.93	145.3	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	20	1.0000000	1.1000000	10
ALIVE	2	FEMALE	75	III	4.42	4.30	12.50	209	0.06	3.40	174.0	70.2	17.40	17.10	4.04	98	25.96	64.40	2.45	2.55	2.52	134.9	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERTENSION	MODERATE	DOSE REDUCED	2	15	0.9736842	0.6973684	41
ALIVE	6	MALE	74	III	7.43	4.12	11.40	126	0.67	3.97	205.0	61.1	15.80	9.10	3.82	95	20.85	51.60	2.00	2.20	2.00	125.0	PANCREAS CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	MODERATE	DOSE REDUCED	3	35	0.8391608	0.9895105	3
ALIVE	3	FEMALE	76	IV	4.09	3.64	11.50	106	0.01	3.70	199.0	63.1	21.60	16.10	4.26	116	20.90	43.40	2.22	1.59	2.28	79.0	LUNG CANCER	CHEMO	ASYMTOMATIC	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	3	0	1.0094787	1.1611374	32
ALIVE	3	MALE	77	III	9.01	4.08	12.50	463	0.04	4.10	205.0	56.5	43.70	96.50	4.52	111	22.94	60.20	2.68	2.30	2.59	160.8	LUNG CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	HIGHT SEVERE	DOSE REDUCED	3	6	1.0464135	1.2573840	15
ALIVE	4	MALE	68	II	7.34	4.14	9.60	187	3.01	2.92	214.0	94.2	17.90	13.90	4.13	106	21.57	59.50	2.28	2.41	2.04	90.7	LUNG CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	4	0	1.0711111	0.6933333	14
ALIVE	2	MALE	68	II	6.53	5.16	15.40	247	0.20	4.10	157.0	58.4	17.90	15.50	4.48	119	35.81	94.20	2.76	2.64	3.09	178.5	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	4	0	1.0836653	1.0079681	15
ALIVE	3	FEMALE	86	IV	7.99	3.87	11.80	144	6.59	3.96	143.0	41.0	47.80	36.10	3.69	123	26.75	59.00	2.34	1.76	2.60	114.9	UROTHELIAL CARCINOMA	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERTENSION	SEVERE	DOSE REDUCED	5	12	0.7651246	0.8718861	24
ALIVE	2	MALE	74	II	5.37	4.40	14.20	214	0.02	4.10	133.0	61.1	19.60	17.20	3.93	74	19.62	49.60	2.40	2.50	2.80	112.4	PANCREAS CANCER	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	SEVERE	DOSE REDUCED	5	0	0.9000000	0.6769231	6
ALIVE	3	FEMALE	52	I	2.31	3.68	11.60	159	0.17	4.14	183.0	65.8	18.70	18.00	4.75	95	22.19	56.30	1.98	2.03	2.31	146.2	EWING SARCOMA	CHEMO	ASYMTOMATIC	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	5	0	1.2162162	1.0450450	32
ALIVE	4	FEMALE	74	IV	1.72	3.35	11.60	121	0.02	3.60	149.0	96.4	21.80	27.00	4.50	90	17.78	40.00	1.96	2.32	2.08	125.3	HEAD AND NECK	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERTENSION	MODERATE	DOSE NOT CHANGED	6	1	0.8571429	0.8928571	23
ALIVE	13	FEMALE	70	IV	5.04	4.09	11.90	325	2.01	3.23	494.0	79.7	38.20	23.20	4.48	84	24.33	54.60	2.31	3.20	1.98	164.0	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	NO SIDE EFFECTS	UNKNOW	7	0	1.0594796	0.7211896	15
ALIVE	2	MALE	62	III	7.05	5.43	15.60	235	0.06	4.15	178.0	67.4	24.70	29.20	4.10	93	16.24	47.20	2.33	1.92	2.01	84.6	LUNG CANCER	CHEMO	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	8	0	1.0347490	0.8880309	63
ALIVE	3	MALE	79	IV	5.69	3.99	12.40	193	0.15	3.75	222.0	53.7	123.10	29.80	4.27	201	20.64	59.10	2.89	3.31	3.78	104.2	RENAL CANCER	CHEMO	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	9	0	1.0340426	1.0851064	9
ALIVE	14	FEMALE	69	II	4.56	4.43	13.70	294	0.17	4.53	193.0	79.0	23.20	32.90	3.90	161	18.57	45.20	2.43	2.55	2.35	96.7	PANCREAS CANCER	CHEMO	SYMPTOMATIC >50 % IN THE BED	ATEROESCLEROSIS	NO SIDE EFFECTS	DOSE REDUCED	9	0	0.9725490	0.9882353	4
ALIVE	10	FEMALE	65	II	13.30	2.86	8.70	140	0.60	4.32	161.0	74.0	23.10	33.10	4.18	83	16.51	41.00	2.70	2.32	2.22	59.0	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERLIPIDEMIA	SEVERE	DOSE REDUCED	10	32	1.0336538	1.2067308	21
ALIVE	4	MALE	74	III	2.41	3.36	10.20	311	0.14	3.76	242.0	73.5	32.80	33.50	4.43	86	22.08	63.80	2.44	2.59	2.78	164.3	LUNG CANCER	CHEMO	ASYMTOMATIC	HYPERTENSION	MODERATE	DOSE NOT CHANGED	12	36	1.0897959	1.1673469	3
ALIVE	12	MALE	76	III	6.51	4.32	13.30	215	0.05	3.91	184.0	55.2	15.20	10.60	4.40	181	26.80	77.90	2.65	2.30	2.33	261.4	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	ATEROESCLEROSIS	SEVERE	ADDED OTHER TREATMEN	14	51	0.7738516	0.6961131	6
ALIVE	4	FEMALE	72	III	1.52	3.75	11.10	244	0.02	3.88	174.0	72.2	21.40	13.30	4.02	116	15.43	39.20	2.89	2.07	1.88	50.8	EWING SARCOMA	CHEMO	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	ADDED OTHER TREATMEN	17	0	1.3398693	1.8954248	20
ALIVE	14	FEMALE	76	III	1.67	2.65	8.30	194	3.91	3.98	96.0	63.1	6.10	3.70	4.69	179	29.56	68.30	2.48	2.98	2.37	172.7	GINECOLOGICAL	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	HYPERTENSION	NO SIDE EFFECTS	UNKNOW	19	0	1.3386243	0.9947090	20
ALIVE	4	FEMALE	56	III	7.27	4.34	13.90	189	0.13	4.27	182.0	73.5	26.10	14.30	4.18	80	18.03	48.20	2.48	2.20	1.95	96.5	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	UNKNOW	20	0	0.8888889	1.0000000	1
ALIVE	4	MALE	70	III	4.86	4.30	14.50	214	0.59	4.04	176.0	59.9	26.50	29.50	3.78	262	23.75	68.40	2.20	3.50	2.40	81.8	LUNG CANCER	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DOSE NOT CHANGED	20	0	1.3288591	1.4765101	3
ALIVE	4	FEMALE	75	III	6.33	4.19	13.00	351	0.02	4.62	178.0	72.3	22.30	18.10	4.25	100	25.78	50.90	2.10	2.60	2.69	96.9	GINECOLOGICAL	CHEMO	ASYMTOMATIC	HYPERTENSION	SEVERE	DOSE REDUCED	33	312	0.9138577	0.7715356	5
DEATH	9	MALE	81	II	6.66	4.27	12.70	315	0.35	4.19	204.0	77.2	25.50	19.50	4.67	81	16.17	39.10	2.11	3.09	2.70	123.7	GASTRIC CANCER	CHEMO	ASYMTOMATIC	CANCER RRECURRENCE	MODERATE	DRUG INTERRUPTED	9	86	0.7684564	0.8087248	22
DEATH	9	MALE	73	III	7.95	2.72	8.80	132	6.37	4.30	157.0	210.6	53.30	34.80	3.11	150	16.90	41.50	2.20	2.00	2.20	210.3	PANCREAS CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	4	195	0.8035191	0.8914956	16
DEATH	5	MALE	77	I	7.43	4.72	15.00	222	0.39	3.80	151.0	69.2	24.60	21.20	4.28	101	25.21	67.80	2.81	2.85	2.87	116.0	LUNG CANCER	CHEMO	ASYMTOMATIC	CHRONIC INFLAMMATION	NO SIDE EFFECTS	DRUG WIHDRAWN	5	0	1.1666667	1.6161616	9
DEATH	6	MALE	74	I	4.71	4.69	14.70	170	0.55	3.52	210.0	76.6	21.90	14.20	4.71	140	23.51	60.40	2.58	2.26	3.20	138.6	GASTRIC CANCER	CHEMO	ASYMTOMATIC	CANCER RRECURRENCE	NO SIDE EFFECTS	DOSE NOT CHANGED	12	0	0.8776978	0.9316547	1
DEATH	6	MALE	70	I	5.70	4.31	13.20	200	0.21	3.94	216.0	74.8	22.60	16.80	4.08	83	16.68	45.70	2.41	1.56	2.25	112.0	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DOSE REDUCED	12	50	0.9790941	0.9930314	13
DEATH	6	MALE	63	II	4.08	2.99	8.20	163	0.18	3.68	142.0	62.5	94.20	114.80	4.93	196	17.89	50.60	2.85	1.94	2.69	145.3	PANCREAS CANCER	CHEMO	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE REDUCED	7	0	0.9722222	1.0119048	64
DEATH	5	FEMALE	61	II	6.55	5.21	15.80	203	0.02	4.27	214.0	53.8	21.10	28.50	3.97	75	20.44	50.70	2.72	2.53	2.51	150.2	GINECOLOGICAL	CHEMO	ASYMTOMATIC	HYPERLIPIDEMIA	HIGHT SEVERE	DOSE REDUCED	11	114	1.1176471	1.1813725	12
DEATH	2	MALE	79	III	7.83	4.55	13.40	262	0.26	4.35	196.0	71.3	24.30	27.00	4.37	109	17.30	45.40	2.25	2.19	2.86	191.5	LUNG CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	CANCER RRECURRENCE	SEVERE	DOSE REDUCED	3	0	1.5372340	1.1010638	17
DEATH	3	FEMALE	68	III	3.45	3.97	12.80	193	0.04	3.70	226.0	84.5	21.40	20.50	4.41	93	25.78	54.20	2.16	2.60	3.33	90.6	SARCOMA	CHEMO	ASYMTOMATIC	ATEROESCLEROSIS	MODERATE	DRUG INTERRUPTED	9	7	0.6486486	0.7807808	16
DEATH	6	FEMALE	67	III	7.08	4.46	12.20	459	0.28	3.69	221.0	73.7	27.81	27.18	4.32	130	19.07	44.00	2.51	2.31	2.54	97.2	HEAD AND NECK	CHEMO	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE REDUCED	1	0	0.7713311	0.7849829	47
DEATH	10	MALE	87	III	1.59	3.94	12.00	78	3.34	3.21	204.0	69.2	23.40	16.30	4.37	94	23.76	59.70	2.38	2.90	2.73	148.4	GASTRIC CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERTENSION	SEVERE	DOSE REDUCED	10	33	0.9166667	1.4583333	30
DEATH	4	MALE	78	III	7.24	4.95	13.80	210	0.77	4.18	142.0	89.2	15.70	19.60	3.97	94	19.86	55.40	2.15	2.45	2.81	146.9	LUNG CANCER	CHEMO	BEDBOUND	CHRONIC INFLAMMATION	MODERATE	DRUG INTERRUPTED	8	7	0.8571429	0.8787879	32
DEATH	8	FEMALE	53	I	3.97	4.20	12.40	222	0.02	3.95	150.0	85.1	13.60	14.90	4.09	94	19.26	51.80	2.13	2.45	2.11	173.0	PANCREAS CANCER	CHEMO	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SLIGTHLY SIDE EFFCTS	DOSE REDUCED	3	7	0.8717949	1.0622711	23

Exploracion inicial

# Tipos de varibales                    
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux

A data.frame: 34 × 2

	Tipo de Variable	Numero_Columna
	<chr>	<int>
STATUS	character	1
CYCLES_BETWEEN_PET1_PET2	numeric	2
GENDER	character	3
AGE	numeric	4
TNM_STAGE	character	5
WBC_PRE	numeric	6
RBC_PRE	numeric	7
HB_PRE	numeric	8
PLT_PRE	numeric	9
CRP_PRE	numeric	10
ALBUMIN_PRE	numeric	11
LDH_PRE	numeric	12
eGFR_PRE	numeric	13
AST_PRE	numeric	14
ALT_PRE	numeric	15
K_PRE	numeric	16
BGL_PRE_PET1	numeric	17
BMI	numeric	18
BW_PRE	numeric	19
SPLEEN_FDG_UPT_PRE_PET1	numeric	20
BM_UPT_PRE_PET1	numeric	21
LIVER_PRE_PET1	numeric	22
ESTIMATED_SPLEE_VOL_PRE_PET1	numeric	23
DIAGNOSTIC	character	24
TREATMENT	character	25
ECOGPS	character	26
COMORBIDITIES	character	27
CTCNCI	character	28
ACTION_TAKEN_	character	29
TIME_BETWEE_PET	numeric	30
dias	numeric	31
SLR_pre	numeric	32
BMLR_pre	numeric	33
OVERALL_TIME	numeric	34

overview(datos)  )

plot(overview(datos))

A overview: 16 × 3

division	metrics	value
<chr>	<chr>	<dbl>
size	observations	59
size	variables	34
size	values	2006
size	memory size	23752
duplicated	duplicate observation	0
missing	complete observation	59
missing	missing observation	0
missing	missing variables	0
missing	missing values	0
data type	numerics	25
data type	integers	0
data type	factors/ordered	0
data type	characters	9
data type	Dates	0
data type	POSIXcts	0
data type	others	0

ORDENAR Y LIMPIAR DATOS

Transformar tipos de variables

# pasar variables a factor
datos$STATUS <- as.factor(datos$STATUS)
datos$GENDER <- as.factor(datos$GENDER)
datos$COMORBIDITIES <- as.factor(datos$COMORBIDITIES)
datos$CTCNCI <- as.factor(datos$CTCNCI)
datos$ACTION_TAKEN_ <- as.factor(datos$ACTION_TAKEN_)
datos$TNM_STAGE <- as.factor(datos$TNM_STAGE)
datos$DIAGNOSTIC <- as.factor(datos$DIAGNOSTIC)
datos$TREATMENT <- as.factor(datos$TREATMENT)
datos$ECOGPS <- as.factor(datos$ECOGPS)

# vemos los cambios
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux

A data.frame: 34 × 2

	Tipo de Variable	Numero_Columna
	<chr>	<int>
STATUS	factor	1
CYCLES_BETWEEN_PET1_PET2	numeric	2
GENDER	factor	3
AGE	numeric	4
TNM_STAGE	factor	5
WBC_PRE	numeric	6
RBC_PRE	numeric	7
HB_PRE	numeric	8
PLT_PRE	numeric	9
CRP_PRE	numeric	10
ALBUMIN_PRE	numeric	11
LDH_PRE	numeric	12
eGFR_PRE	numeric	13
AST_PRE	numeric	14
ALT_PRE	numeric	15
K_PRE	numeric	16
BGL_PRE_PET1	numeric	17
BMI	numeric	18
BW_PRE	numeric	19
SPLEEN_FDG_UPT_PRE_PET1	numeric	20
BM_UPT_PRE_PET1	numeric	21
LIVER_PRE_PET1	numeric	22
ESTIMATED_SPLEE_VOL_PRE_PET1	numeric	23
DIAGNOSTIC	factor	24
TREATMENT	factor	25
ECOGPS	factor	26
COMORBIDITIES	factor	27
CTCNCI	factor	28
ACTION_TAKEN_	factor	29
TIME_BETWEE_PET	numeric	30
dias	numeric	31
SLR_pre	numeric	32
BMLR_pre	numeric	33
OVERALL_TIME	numeric	34

Identificar la variable respuesta

# Identificar variable respuesta
respuesta <- c("STATUS")
respuesta

'STATUS'

Identificar las variables cuantitativas de entrada

# Identificar variables numericas de entrada
tipos_var <- t(t(sapply(datos, class)))
var_num <- colnames(datos)[tipos_var=="integer"|tipos_var=="numeric"]
var_num <- var_num[var_num!=respuesta]
as.data.frame(var_num)

A data.frame: 25 × 1

var_num
<chr>
CYCLES_BETWEEN_PET1_PET2
AGE
WBC_PRE
RBC_PRE
HB_PRE
PLT_PRE
CRP_PRE
ALBUMIN_PRE
LDH_PRE
eGFR_PRE
AST_PRE
ALT_PRE
K_PRE
BGL_PRE_PET1
BMI
BW_PRE
SPLEEN_FDG_UPT_PRE_PET1
BM_UPT_PRE_PET1
LIVER_PRE_PET1
ESTIMATED_SPLEE_VOL_PRE_PET1
TIME_BETWEE_PET
dias
SLR_pre
BMLR_pre
OVERALL_TIME

Identificar las variables cualitativas de entrada

# Identificar variables cualitativas
tipos_var <- t(t(sapply(datos, class)))
var_cual <- colnames(datos)[tipos_var=="character"|tipos_var=="factor"]
var_cual <- var_cual[var_cual!=respuesta]
as.data.frame(var_cual)

A data.frame: 8 × 1

var_cual
<chr>
GENDER
TNM_STAGE
DIAGNOSTIC
TREATMENT
ECOGPS
COMORBIDITIES
CTCNCI
ACTION_TAKEN_

EXPLORACION UNIVARIADA DE LOS DATOS

Describir la variable respuesta

# Tabla de frecuencias:

as.data.frame(table(datos$STATUS))

A data.frame: 2 × 2

Var1	Freq
<fct>	<int>
ALIVE	38
DEATH	21

# Barplot
barplot(table(datos$STATUS))

Descripcion variables cuantitativas de entrada

Descriptivo numerico variables numericas

# Descripcion de variables cuantitativas:
d_uni<-dlookr::describe(datos)
d_uni

A tibble: 25 × 26

described_variables	n	na	mean	sd	se_mean	IQR	skewness	kurtosis	p00	p01	p05	p10	p20	p25	p30	p40	p50	p60	p70	p75	p80	p90	p95	p99	p100
<chr>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
CYCLES_BETWEEN_PET1_PET2	59	0	7.0677966	7.4506755	0.96999533	4.5000000	3.54613621	16.07974075	2.0000000	2.0000000	2.0000000	2.0000000	3.0000000	3.0000000	3.400000	4.0000000	5.0000000	5.800000	6.000000	7.500000	9.400000	14.000000	18.200000	36.980000	48.000000
AGE	59	0	69.3898305	8.6223786	1.12253807	12.5000000	-0.19664396	-0.58941186	52.0000000	52.5800000	54.0000000	56.0000000	61.0000000	63.5000000	65.000000	68.0000000	70.0000000	72.800000	74.600000	76.000000	76.400000	79.000000	83.000000	86.420000	87.000000
WBC_PRE	59	0	5.9266102	2.5757621	0.33533566	3.2150000	0.62790197	0.82592662	1.5200000	1.5606000	1.7150000	2.8420000	3.9100000	4.1700000	4.504000	4.8960000	5.7000000	6.546000	7.184000	7.385000	7.626000	8.730000	9.907000	13.308400	13.320000
RBC_PRE	59	0	4.0798305	0.6534959	0.08507792	0.6950000	-0.49264321	0.51697560	2.2500000	2.4820000	2.8460000	3.2780000	3.6520000	3.7250000	3.864000	3.9980000	4.1200000	4.294000	4.384000	4.420000	4.496000	4.846000	5.163000	5.302400	5.430000
HB_PRE	59	0	12.1925424	2.0760802	0.27028262	2.7000000	-0.26425199	-0.44892147	7.3000000	7.8220000	8.5340000	9.4400000	10.5600000	11.0500000	11.440000	11.6200000	12.1000000	12.660000	13.360000	13.750000	14.020000	15.000000	15.420000	15.742000	15.800000
PLT_PRE	59	0	241.1016949	107.5321435	13.99949266	107.0000000	1.31603975	1.49043972	78.0000000	94.2400000	125.5000000	140.0000000	158.6000000	168.5000000	180.000000	194.8000000	214.0000000	232.400000	261.200000	275.500000	312.600000	374.000000	466.200000	542.720000	581.000000
CRP_PRE	59	0	1.3561017	2.9053565	0.37824520	0.6450000	4.12883162	21.53706019	0.0100000	0.0158000	0.0200000	0.0200000	0.0600000	0.0750000	0.112000	0.1820000	0.2500000	0.358000	0.596000	0.720000	1.578000	3.950000	6.392000	12.035400	18.630000
ALBUMIN_PRE	59	0	3.8081356	0.5376016	0.06998976	0.4850000	-1.28325605	1.78142566	2.1300000	2.1590000	2.7220000	3.1520000	3.4840000	3.6850000	3.700000	3.7680000	3.9400000	3.978000	4.124000	4.170000	4.222000	4.326000	4.470000	4.567800	4.620000
LDH_PRE	59	0	210.5559322	126.6121968	16.48350401	61.5000000	2.94110478	8.75125622	77.8000000	88.3560000	123.6000000	132.4000000	142.6000000	150.5000000	157.400000	174.0000000	182.0000000	195.800000	204.600000	212.000000	214.000000	244.200000	501.900000	704.020000	751.000000
eGFR_PRE	59	0	71.1915254	23.4414552	3.05181753	18.9000000	3.74520403	21.28582242	39.8000000	40.4960000	46.7800000	51.9000000	56.9800000	58.0000000	60.500000	65.7000000	69.2000000	73.260000	75.940000	76.900000	79.780000	89.200000	96.420000	154.572000	210.600000
AST_PRE	59	0	28.5340678	34.7113610	4.51903428	9.1000000	5.23784777	31.12051661	6.1000000	9.4640000	13.0000000	13.6000000	15.3000000	16.3000000	17.780000	19.2000000	21.2000000	22.220000	24.480000	25.400000	26.260000	39.300000	57.390000	177.322000	252.200000
ALT_PRE	59	0	26.9200000	38.1925396	4.97224514	14.1000000	5.57835083	35.34995715	3.2000000	3.4900000	7.8700000	9.4200000	13.5400000	14.1000000	14.540000	16.2200000	18.0000000	20.460000	27.000000	28.200000	29.620000	33.760000	46.190000	184.814000	281.500000
K_PRE	59	0	4.2564407	0.3753149	0.04886183	0.5650000	-0.13117635	0.30294823	3.1100000	3.4464000	3.7770000	3.8300000	3.9200000	3.9700000	3.996000	4.1060000	4.2600000	4.370000	4.492000	4.535000	4.568000	4.710000	4.816000	5.022400	5.150000
BGL_PRE_PET1	59	0	111.5084746	35.3081865	4.59673434	23.0000000	2.14777101	5.45384366	74.0000000	74.5800000	79.7000000	83.0000000	88.8000000	93.0000000	94.000000	96.4000000	100.0000000	104.000000	111.600000	116.000000	125.000000	161.200000	182.500000	226.620000	262.000000
BMI	59	0	21.5445763	4.0359947	0.52544175	5.5200000	0.78269751	1.39935537	14.0800000	14.8630000	16.2330000	16.6460000	17.9740000	18.2350000	19.146000	20.1780000	21.3400000	22.646000	23.362000	23.755000	24.430000	26.118000	27.373000	32.185000	35.810000
BW_PRE	59	0	55.6759322	12.8068343	1.66730781	16.4500000	-0.03340815	1.74952652	14.0800000	25.3436000	39.1900000	41.4000000	45.3200000	47.2500000	49.800000	51.8000000	56.3000000	59.080000	61.360000	63.700000	65.000000	68.440000	76.010000	86.196000	94.200000
SPLEEN_FDG_UPT_PRE_PET1	59	0	2.3613559	0.2563703	0.03337657	0.3700000	0.36335000	-0.78951320	1.9600000	1.9716000	1.9980000	2.0260000	2.1420000	2.1500000	2.194000	2.2520000	2.3400000	2.408000	2.480000	2.520000	2.608000	2.724000	2.814000	2.890000	2.890000
BM_UPT_PRE_PET1	59	0	2.3925424	0.4596385	0.05983984	0.6050000	-0.08071006	0.02245594	1.3100000	1.3622000	1.5870000	1.8480000	2.0000000	2.0650000	2.200000	2.3020000	2.4100000	2.518000	2.600000	2.670000	2.752000	2.916000	3.101000	3.389800	3.500000
LIVER_PRE_PET1	59	0	2.4950847	0.4375495	0.05696409	0.5800000	0.25199343	0.57543184	1.4900000	1.5132000	1.8890000	1.9960000	2.0980000	2.2100000	2.264000	2.3540000	2.5100000	2.600000	2.718000	2.790000	2.824000	2.940000	3.213000	3.565400	3.780000
ESTIMATED_SPLEE_VOL_PRE_PET1	59	0	145.3847458	58.4264814	7.60647999	69.6500000	0.92842859	0.62550270	50.8000000	54.3380000	77.0000000	84.0400000	96.8200000	103.2000000	112.160000	122.4200000	138.6000000	146.760000	164.180000	172.850000	180.580000	230.980000	261.520000	295.910000	316.500000
TIME_BETWEE_PET	59	0	9.6271186	7.6404060	0.99469613	8.0000000	1.68704563	3.31772530	1.0000000	1.0000000	2.0000000	2.8000000	4.0000000	4.0000000	5.000000	6.0000000	8.0000000	9.000000	10.600000	12.000000	13.400000	20.000000	25.000000	35.100000	38.000000
dias	59	0	57.1525424	133.9282893	17.43597813	34.0000000	3.29497169	12.07515448	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.000000	0.0000000	0.0000000	7.000000	26.800000	34.000000	50.400000	202.600000	363.100000	564.480000	735.000000
SLR_pre	59	0	0.9711074	0.1795212	0.02337167	0.2322534	0.78999558	0.67146681	0.6486486	0.6843759	0.7629179	0.7707561	0.8024302	0.8330420	0.857764	0.8945631	0.9725490	1.000000	1.034466	1.065295	1.100936	1.176577	1.329836	1.422762	1.537234
BMLR_pre	59	0	0.9821935	0.2419472	0.03149884	0.2486236	1.17315435	3.09672332	0.4946996	0.5424875	0.6916923	0.7044737	0.7833021	0.8456242	0.876912	0.9091625	0.9871245	1.006375	1.067936	1.094248	1.120513	1.191564	1.460151	1.733452	1.895425
OVERALL_TIME	59	0	19.1016949	15.8415592	2.06239534	15.0000000	1.35374471	1.56391033	1.0000000	1.0000000	1.9000000	3.0000000	6.0000000	8.5000000	10.400000	12.2000000	15.0000000	17.800000	21.600000	23.500000	29.400000	41.200000	51.300000	64.420000	65.000000

Graficos descriptivos variables numericas

for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  r <- list()
  
  # Histograma de densidad
  r[[1]]<-ggplot(datos, aes(x=!!name_i)) +
    geom_density(fill="#69b3a2", color="black", alpha=0.6)+
    theme_light() +
    #theme(legend.position = "none") +
    xlab("") +
    ylab("Densidad de Frecuencia")
  
  # Boxplot
  r[[2]]<-ggplot(data=datos, aes(y=!!name_i)) +
    geom_boxplot(size = 0.4) +
    theme_light() +
    theme(legend.position = "none")+
    xlab("")
  
  # Qqplot
  r[[3]]<-ggqqplot(datos, x = var_num[i],color = "#FF6666",add.params = list(color = "black"))+
    xlab("") + ylab("Cuartiles reales") +
    theme_minimal() +
    #ggtitle("qqplot") +
    theme(plot.title = element_text(hjust = 0.5))
  
  grid.arrange(r[[1]], r[[2]], r[[3]],
               nrow=1, ncol = 3,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Analisis outliers(no los quitamos pero quiza aporten informacion )

# Analisis de outliers:
diag_out <- diagnose_outlier(datos)
diag_out

A data.frame: 25 × 6

variables	outliers_cnt	outliers_ratio	outliers_mean	with_mean	without_mean
<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>
CYCLES_BETWEEN_PET1_PET2	5	8.474576	26.000000	7.0677966	5.3148148
AGE	0	0.000000	NaN	69.3898305	69.3898305
WBC_PRE	2	3.389831	13.310000	5.9266102	5.6675439
RBC_PRE	2	3.389831	2.450000	4.0798305	4.1370175
HB_PRE	0	0.000000	NaN	12.1925424	12.1925424
PLT_PRE	6	10.169492	492.500000	241.1016949	212.6415094
CRP_PRE	12	20.338983	5.622500	1.3561017	0.2668085
ALBUMIN_PRE	6	10.169492	2.586667	3.8081356	3.9464151
LDH_PRE	5	8.474576	594.200000	210.5559322	175.0333333
eGFR_PRE	2	3.389831	162.300000	71.1915254	67.9947368
AST_PRE	6	10.169492	102.383333	28.5340678	20.1737736
ALT_PRE	3	5.084746	164.266667	26.9200000	19.5621429
K_PRE	1	1.694915	3.110000	4.2564407	4.2762069
BGL_PRE_PET1	7	11.864407	191.714286	111.5084746	100.7115385
BMI	1	1.694915	35.810000	21.5445763	21.2986207
BW_PRE	2	3.389831	54.140000	55.6759322	55.7298246
SPLEEN_FDG_UPT_PRE_PET1	0	0.000000	NaN	2.3613559	2.3613559
BM_UPT_PRE_PET1	0	0.000000	NaN	2.3925424	2.3925424
LIVER_PRE_PET1	1	1.694915	3.780000	2.4950847	2.4729310
ESTIMATED_SPLEE_VOL_PRE_PET1	2	3.389831	298.750000	145.3847458	140.0035088
TIME_BETWEE_PET	4	6.779661	30.250000	9.6271186	8.1272727
dias	10	16.949153	293.800000	57.1525424	8.8571429
SLR_pre	1	1.694915	1.537234	0.9711074	0.9613466
BMLR_pre	3	5.084746	1.662699	0.9821935	0.9457379
OVERALL_TIME	5	8.474576	57.800000	19.1016949	15.5185185

# Plot analisis de outliers:
plot_outlier(datos)

# Plot de normalidad:
plot_normality(datos)

Diagrama de correlacion variables numericas

var_num

1. 'CYCLES_BETWEEN_PET1_PET2'
2. 'AGE'
3. 'WBC_PRE'
4. 'RBC_PRE'
5. 'HB_PRE'
6. 'PLT_PRE'
7. 'CRP_PRE'
8. 'ALBUMIN_PRE'
9. 'LDH_PRE'
10. 'eGFR_PRE'
11. 'AST_PRE'
12. 'ALT_PRE'
13. 'K_PRE'
14. 'BGL_PRE_PET1'
15. 'BMI'
16. 'BW_PRE'
17. 'SPLEEN_FDG_UPT_PRE_PET1'
18. 'BM_UPT_PRE_PET1'
19. 'LIVER_PRE_PET1'
20. 'ESTIMATED_SPLEE_VOL_PRE_PET1'
21. 'TIME_BETWEE_PET'
22. 'dias'
23. 'SLR_pre'
24. 'BMLR_pre'
25. 'OVERALL_TIME'

vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
                'AGE',
                'WBC_PRE',
                'RBC_PRE',
                'HB_PRE',
                'PLT_PRE',
                'CRP_PRE',
                'ALBUMIN_PRE',
                'LDH_PRE',
                'eGFR_PRE',
                'AST_PRE',
                'ALT_PRE',
                'K_PRE',
                'BGL_PRE_PET1',
                'BMI',
                'BW_PRE',
                'SPLEEN_FDG_UPT_PRE_PET1',
                'BM_UPT_PRE_PET1',
                'LIVER_PRE_PET1',
                'ESTIMATED_SPLEE_VOL_PRE_PET1',
                'TIME_BETWEE_PET',
                'dias',
                'SLR_pre',
                'BMLR_pre',
                'OVERALL_TIME')

options(repr.plot.width=16, repr.plot.height=10)

plot_correlate(datos[,vec_var_num],method = "spearman")

Coeficiente de correlacion por parejas

# coeficiente de correlación
library(Hmisc)
vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
                'AGE',
                'WBC_PRE',
                'RBC_PRE',
                'HB_PRE',
                'PLT_PRE',
                'CRP_PRE',
                'ALBUMIN_PRE',
                'LDH_PRE',
                'eGFR_PRE',
                'AST_PRE',
                'ALT_PRE',
                'K_PRE',
                'BGL_PRE_PET1',
                'BMI',
                'BW_PRE',
                'SPLEEN_FDG_UPT_PRE_PET1',
                'BM_UPT_PRE_PET1',
                'LIVER_PRE_PET1',
                'ESTIMATED_SPLEE_VOL_PRE_PET1',
                'TIME_BETWEE_PET',
                'dias',
                'SLR_pre',
                'BMLR_pre',
                'OVERALL_TIME')

# Correlaciones por parejas:

flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
  )
}


# Calculamos la matriz de correlacion y visualizamos la correlacion de mayor intensidad
tcor<-rcorr(as.matrix(datos[,vec_var_num]),type = "spearman")
corr_data <- flattenCorrMatrix(tcor$r, tcor$P) %>%
    arrange(desc(abs(cor))) #%>%
    #filter(row=='Sales')
corr_data
options(warn = -1)

A data.frame: 300 × 4

row	column	cor	p
<chr>	<chr>	<dbl>	<dbl>
BMI	BW_PRE	0.8439125610	0.000000e+00
AST_PRE	ALT_PRE	0.7485788612	9.337642e-12
RBC_PRE	HB_PRE	0.7152462328	1.956835e-10
SLR_pre	BMLR_pre	0.4975816473	6.083159e-05
WBC_PRE	PLT_PRE	0.4128417580	1.156029e-03
CYCLES_BETWEEN_PET1_PET2	TIME_BETWEE_PET	0.4059972839	1.420657e-03
RBC_PRE	LIVER_PRE_PET1	0.3953644722	1.940379e-03
HB_PRE	ALBUMIN_PRE	0.3873943684	2.435104e-03
BMI	LIVER_PRE_PET1	0.3772760511	3.223264e-03
BW_PRE	LIVER_PRE_PET1	0.3767317487	3.271446e-03
eGFR_PRE	BW_PRE	-0.3716937022	3.748621e-03
LDH_PRE	AST_PRE	0.3632829124	4.683351e-03
eGFR_PRE	LIVER_PRE_PET1	-0.3573599908	5.459380e-03
RBC_PRE	ALBUMIN_PRE	0.3547613964	5.834072e-03
RBC_PRE	PLT_PRE	0.3502893385	6.531908e-03
HB_PRE	LIVER_PRE_PET1	0.3499144920	6.593589e-03
SPLEEN_FDG_UPT_PRE_PET1	BMLR_pre	0.3396383887	8.495206e-03
WBC_PRE	CRP_PRE	0.3280741334	1.119017e-02
BW_PRE	OVERALL_TIME	-0.3057847098	1.851064e-02
BW_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	0.3043719681	1.908778e-02
AST_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.2969473800	2.237887e-02
BMI	BM_UPT_PRE_PET1	0.2935812057	2.402225e-02
BM_UPT_PRE_PET1	LIVER_PRE_PET1	0.2918323210	2.491552e-02
eGFR_PRE	BMI	-0.2910918880	2.530206e-02
LDH_PRE	OVERALL_TIME	-0.2822208193	3.033945e-02
CYCLES_BETWEEN_PET1_PET2	LIVER_PRE_PET1	-0.2818737216	3.055247e-02
CYCLES_BETWEEN_PET1_PET2	CRP_PRE	0.2702070989	3.847434e-02
WBC_PRE	K_PRE	-0.2673740583	4.063519e-02
LDH_PRE	ALT_PRE	0.2549621514	5.131459e-02
PLT_PRE	BM_UPT_PRE_PET1	0.2540631421	5.216948e-02
BW_PRE	BM_UPT_PRE_PET1	0.2540411018	5.219058e-02
CYCLES_BETWEEN_PET1_PET2	ESTIMATED_SPLEE_VOL_PRE_PET1	0.2536110188	5.260378e-02
TIME_BETWEE_PET	OVERALL_TIME	-0.2510153506	5.515436e-02
SPLEEN_FDG_UPT_PRE_PET1	SLR_pre	0.2473979804	5.887532e-02
AGE	BGL_PRE_PET1	0.2444181044	6.209066e-02
eGFR_PRE	SPLEEN_FDG_UPT_PRE_PET1	-0.2403333203	6.672641e-02
CYCLES_BETWEEN_PET1_PET2	dias	0.2324762154	7.642104e-02
WBC_PRE	RBC_PRE	0.2310951998	7.823513e-02
ALBUMIN_PRE	ALT_PRE	0.2308704321	7.853358e-02
BMI	OVERALL_TIME	-0.2297120028	8.008611e-02
WBC_PRE	BMI	-0.2229349621	8.966081e-02
CRP_PRE	ALBUMIN_PRE	-0.2197259109	9.449689e-02
dias	SLR_pre	-0.2193832537	9.502508e-02
LDH_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.2162525652	9.995807e-02
HB_PRE	BM_UPT_PRE_PET1	0.2143995328	1.029702e-01
WBC_PRE	BW_PRE	-0.2122890334	1.064858e-01
SPLEEN_FDG_UPT_PRE_PET1	LIVER_PRE_PET1	0.2122421613	1.065649e-01
BGL_PRE_PET1	dias	-0.2107065246	1.091822e-01
RBC_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.2089345109	1.122634e-01
BMI	ESTIMATED_SPLEE_VOL_PRE_PET1	0.2072825039	1.151957e-01
CYCLES_BETWEEN_PET1_PET2	HB_PRE	-0.2027624167	1.235186e-01
WBC_PRE	dias	0.2010640813	1.267611e-01
WBC_PRE	ALT_PRE	0.2008621320	1.271510e-01
K_PRE	BW_PRE	0.1997515388	1.293110e-01
AGE	RBC_PRE	-0.1959448874	1.369254e-01
ALBUMIN_PRE	LDH_PRE	-0.1932484395	1.425195e-01
CRP_PRE	BM_UPT_PRE_PET1	0.1930119904	1.430181e-01
K_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.1927480104	1.435762e-01
CYCLES_BETWEEN_PET1_PET2	SLR_pre	-0.1914579704	1.463273e-01
HB_PRE	CRP_PRE	-0.1852301672	1.601625e-01
CRP_PRE	BGL_PRE_PET1	0.1801524940	1.721365e-01
ALT_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.1794231107	1.739087e-01
WBC_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.1776816634	1.781935e-01
AGE	dias	-0.1766211146	1.808401e-01
BGL_PRE_PET1	BW_PRE	0.1755949032	1.834279e-01
ESTIMATED_SPLEE_VOL_PRE_PET1	dias	0.1751759322	1.844921e-01
CYCLES_BETWEEN_PET1_PET2	RBC_PRE	-0.1742457189	1.868707e-01
HB_PRE	BW_PRE	0.1716970981	1.935006e-01
RBC_PRE	BW_PRE	0.1712891253	1.945772e-01
ALBUMIN_PRE	SLR_pre	0.1709591663	1.954512e-01
PLT_PRE	ALT_PRE	0.1680061960	2.033973e-01
LIVER_PRE_PET1	TIME_BETWEE_PET	-0.1674751999	2.048501e-01
BGL_PRE_PET1	SPLEEN_FDG_UPT_PRE_PET1	0.1672762545	2.053963e-01
AST_PRE	BMI	-0.1660456217	2.087978e-01
HB_PRE	BMI	0.1615633147	2.215226e-01
RBC_PRE	BGL_PRE_PET1	-0.1583381375	2.310068e-01
RBC_PRE	BM_UPT_PRE_PET1	0.1581508896	2.315659e-01
eGFR_PRE	BGL_PRE_PET1	-0.1548958383	2.414353e-01
CYCLES_BETWEEN_PET1_PET2	SPLEEN_FDG_UPT_PRE_PET1	-0.1548237804	2.416570e-01
BGL_PRE_PET1	BMI	0.1536830940	2.451850e-01
WBC_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.1524777189	2.489511e-01
AST_PRE	BW_PRE	-0.1524747557	2.489604e-01
AST_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.1487735071	2.607702e-01
PLT_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.1474118229	2.652084e-01
ALBUMIN_PRE	LIVER_PRE_PET1	0.1446593311	2.743336e-01
AGE	BW_PRE	-0.1442156712	2.758237e-01
SPLEEN_FDG_UPT_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.1440704093	2.763128e-01
SPLEEN_FDG_UPT_PRE_PET1	BM_UPT_PRE_PET1	0.1439098232	2.768541e-01
RBC_PRE	CRP_PRE	-0.1427046823	2.809390e-01
AGE	K_PRE	-0.1417137960	2.843274e-01
K_PRE	SLR_pre	0.1415396334	2.849257e-01
HB_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	0.1413496306	2.855794e-01
TIME_BETWEE_PET	BMLR_pre	0.1392753878	2.927798e-01
ALT_PRE	BMI	-0.1392249694	2.929563e-01
CRP_PRE	OVERALL_TIME	0.1385718467	2.952487e-01
K_PRE	BMI	0.1380257850	2.971743e-01
ALT_PRE	BW_PRE	-0.1373586229	2.995381e-01
CYCLES_BETWEEN_PET1_PET2	eGFR_PRE	0.1369027236	3.011603e-01
CYCLES_BETWEEN_PET1_PET2	ALBUMIN_PRE	-0.1359600225	3.045328e-01
AGE	ALBUMIN_PRE	-0.1353001075	3.069081e-01
BM_UPT_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	0.1352741756	3.070017e-01
ALBUMIN_PRE	AST_PRE	0.1341869884	3.109416e-01
ALBUMIN_PRE	dias	0.1323828851	3.175510e-01
RBC_PRE	BMI	0.1308083465	3.233920e-01
HB_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.1295180732	3.282290e-01
ALBUMIN_PRE	BMLR_pre	0.1288422529	3.307806e-01
LIVER_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	0.1287593170	3.310946e-01
WBC_PRE	LIVER_PRE_PET1	-0.1276067171	3.354778e-01
SPLEEN_FDG_UPT_PRE_PET1	dias	-0.1272656050	3.367819e-01
K_PRE	dias	-0.1265551229	3.395084e-01
K_PRE	BGL_PRE_PET1	-0.1255174657	3.435151e-01
HB_PRE	TIME_BETWEE_PET	0.1253225949	3.442708e-01
CYCLES_BETWEEN_PET1_PET2	BMI	-0.1205068566	3.632736e-01
CYCLES_BETWEEN_PET1_PET2	LDH_PRE	-0.1173257742	3.761695e-01
TIME_BETWEE_PET	dias	0.1173197932	3.761940e-01
SLR_pre	OVERALL_TIME	-0.1170690589	3.772221e-01
RBC_PRE	TIME_BETWEE_PET	-0.1154485948	3.839068e-01
BMLR_pre	OVERALL_TIME	-0.1154116383	3.840601e-01
BMI	BMLR_pre	-0.1146439113	3.872524e-01
LDH_PRE	K_PRE	0.1142915619	3.887227e-01
K_PRE	LIVER_PRE_PET1	0.1142163892	3.890369e-01
ALBUMIN_PRE	OVERALL_TIME	-0.1140136701	3.898847e-01
AST_PRE	BGL_PRE_PET1	0.1139581469	3.901171e-01
AGE	LIVER_PRE_PET1	0.1132407358	3.931274e-01
PLT_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.1131893505	3.933436e-01
ALBUMIN_PRE	BM_UPT_PRE_PET1	-0.1106384229	4.041614e-01
ALT_PRE	dias	0.1089060758	4.116056e-01
ALT_PRE	BMLR_pre	0.1086270384	4.128121e-01
CRP_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	0.1077236060	4.167321e-01
AGE	ALT_PRE	-0.1058722674	4.248315e-01
CYCLES_BETWEEN_PET1_PET2	BW_PRE	-0.1056289159	4.259027e-01
AST_PRE	LIVER_PRE_PET1	0.1045670443	4.305951e-01
CRP_PRE	dias	0.1037107131	4.344004e-01
LIVER_PRE_PET1	BMLR_pre	0.1030980571	4.371344e-01
AGE	eGFR_PRE	-0.1014894087	4.443588e-01
WBC_PRE	SLR_pre	-0.1007101733	4.478820e-01
HB_PRE	eGFR_PRE	-0.0989578022	4.558613e-01
ALT_PRE	SLR_pre	0.0986366227	4.573322e-01
CRP_PRE	SLR_pre	-0.0985596489	4.576851e-01
AST_PRE	TIME_BETWEE_PET	-0.0983011275	4.588714e-01
K_PRE	TIME_BETWEE_PET	0.0961194160	4.689493e-01
AST_PRE	dias	-0.0960692168	4.691826e-01
CYCLES_BETWEEN_PET1_PET2	AST_PRE	-0.0943172454	4.773631e-01
AST_PRE	K_PRE	0.0932955272	4.821686e-01
PLT_PRE	BMI	-0.0918987639	4.887794e-01
BMI	dias	0.0914683960	4.908258e-01
PLT_PRE	eGFR_PRE	0.0911761268	4.922181e-01
RBC_PRE	dias	-0.0908125527	4.939530e-01
HB_PRE	PLT_PRE	0.0901702865	4.970254e-01
BGL_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	0.0885333953	5.049006e-01
AGE	BMLR_pre	-0.0882560071	5.062414e-01
ALT_PRE	OVERALL_TIME	0.0878078224	5.084117e-01
CRP_PRE	BMLR_pre	-0.0865356727	5.145977e-01
CRP_PRE	eGFR_PRE	0.0864847654	5.148460e-01
CYCLES_BETWEEN_PET1_PET2	AGE	-0.0860422553	5.170072e-01
eGFR_PRE	K_PRE	-0.0837815725	5.281190e-01
PLT_PRE	BMLR_pre	-0.0835257330	5.293840e-01
HB_PRE	K_PRE	-0.0827302897	5.333264e-01
eGFR_PRE	dias	0.0826329642	5.338098e-01
HB_PRE	LDH_PRE	-0.0826157512	5.338953e-01
WBC_PRE	ALBUMIN_PRE	0.0813552420	5.401754e-01
LDH_PRE	BGL_PRE_PET1	-0.0798092665	5.479266e-01
AST_PRE	BM_UPT_PRE_PET1	0.0796562365	5.486968e-01
AST_PRE	BMLR_pre	0.0793915218	5.500302e-01
ALT_PRE	LIVER_PRE_PET1	0.0790173616	5.519177e-01
LDH_PRE	TIME_BETWEE_PET	-0.0782925130	5.555829e-01
BW_PRE	SLR_pre	0.0775064662	5.595707e-01
ALBUMIN_PRE	BGL_PRE_PET1	-0.0760678865	5.669036e-01
HB_PRE	dias	-0.0756697955	5.689407e-01
AGE	SLR_pre	-0.0753420125	5.706205e-01
WBC_PRE	LDH_PRE	-0.0750376381	5.721825e-01
RBC_PRE	OVERALL_TIME	-0.0736094218	5.795379e-01
WBC_PRE	eGFR_PRE	0.0733585658	5.808343e-01
PLT_PRE	BW_PRE	-0.0731382979	5.819737e-01
K_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	0.0690128925	6.034986e-01
CYCLES_BETWEEN_PET1_PET2	WBC_PRE	0.0687631395	6.048128e-01
LDH_PRE	dias	0.0684703621	6.063550e-01
AGE	PLT_PRE	-0.0656582318	6.212543e-01
PLT_PRE	K_PRE	0.0654506134	6.223604e-01
LDH_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.0652259740	6.235582e-01
PLT_PRE	AST_PRE	0.0650893627	6.242870e-01
CRP_PRE	K_PRE	-0.0648803684	6.254028e-01
BGL_PRE_PET1	SLR_pre	0.0644944481	6.274653e-01
ALBUMIN_PRE	K_PRE	-0.0642242074	6.289112e-01
BW_PRE	TIME_BETWEE_PET	0.0641836771	6.291282e-01
eGFR_PRE	BM_UPT_PRE_PET1	-0.0634335998	6.331494e-01
WBC_PRE	BM_UPT_PRE_PET1	-0.0632682004	6.340375e-01
ALT_PRE	BGL_PRE_PET1	0.0629725704	6.356262e-01
RBC_PRE	K_PRE	-0.0624963474	6.381888e-01
SPLEEN_FDG_UPT_PRE_PET1	OVERALL_TIME	-0.0624460629	6.384597e-01
BW_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.0610299855	6.461059e-01
RBC_PRE	LDH_PRE	-0.0609196427	6.467033e-01
AGE	BM_UPT_PRE_PET1	0.0607222570	6.477724e-01
PLT_PRE	LDH_PRE	0.0595767085	6.539911e-01
TIME_BETWEE_PET	SLR_pre	0.0589115750	6.576126e-01
BGL_PRE_PET1	LIVER_PRE_PET1	0.0586050199	6.592843e-01
BM_UPT_PRE_PET1	TIME_BETWEE_PET	0.0581153140	6.619583e-01
AGE	CRP_PRE	0.0574017740	6.658621e-01
ALT_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.0573170235	6.663263e-01
WBC_PRE	TIME_BETWEE_PET	-0.0572108123	6.669083e-01
LDH_PRE	SLR_pre	0.0570618427	6.677249e-01
ALT_PRE	K_PRE	0.0565624098	6.704655e-01
eGFR_PRE	ALT_PRE	0.0562764870	6.720364e-01
LIVER_PRE_PET1	dias	0.0551007066	6.785108e-01
BM_UPT_PRE_PET1	dias	-0.0546664511	6.809079e-01
ALT_PRE	TIME_BETWEE_PET	-0.0541064589	6.840038e-01
LDH_PRE	BMLR_pre	0.0539169852	6.850524e-01
BW_PRE	dias	0.0502667548	7.053680e-01
BMI	SLR_pre	-0.0499452035	7.071677e-01
PLT_PRE	OVERALL_TIME	-0.0477729459	7.193670e-01
AGE	AST_PRE	0.0469036023	7.242690e-01
PLT_PRE	dias	-0.0466776365	7.255449e-01
CRP_PRE	LDH_PRE	0.0460435914	7.291292e-01
BGL_PRE_PET1	TIME_BETWEE_PET	0.0458677149	7.301244e-01
BM_UPT_PRE_PET1	OVERALL_TIME	-0.0456008012	7.316357e-01
CRP_PRE	ALT_PRE	-0.0443658168	7.386412e-01
CRP_PRE	BMI	-0.0435164180	7.434718e-01
BGL_PRE_PET1	OVERALL_TIME	0.0433904559	7.441890e-01
PLT_PRE	ALBUMIN_PRE	-0.0433397175	7.444780e-01
PLT_PRE	TIME_BETWEE_PET	0.0432688552	7.448816e-01
eGFR_PRE	BMLR_pre	-0.0429623570	7.466281e-01
AGE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.0425407705	7.490325e-01
WBC_PRE	AST_PRE	0.0423924480	7.498790e-01
WBC_PRE	BGL_PRE_PET1	0.0414448808	7.552938e-01
ALBUMIN_PRE	TIME_BETWEE_PET	0.0406423946	7.598886e-01
WBC_PRE	HB_PRE	-0.0396386929	7.656473e-01
eGFR_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.0388707039	7.700620e-01
PLT_PRE	LIVER_PRE_PET1	-0.0388448836	7.702106e-01
eGFR_PRE	AST_PRE	-0.0380989960	7.745055e-01
ALT_PRE	BM_UPT_PRE_PET1	0.0376779404	7.769330e-01
CRP_PRE	LIVER_PRE_PET1	-0.0374232302	7.784025e-01
CYCLES_BETWEEN_PET1_PET2	OVERALL_TIME	0.0373613855	7.787594e-01
K_PRE	BMLR_pre	-0.0324310903	8.073531e-01
BMI	TIME_BETWEE_PET	0.0323984812	8.075431e-01
BM_UPT_PRE_PET1	SLR_pre	-0.0306338701	8.178411e-01
RBC_PRE	eGFR_PRE	-0.0306006138	8.180354e-01
AST_PRE	OVERALL_TIME	-0.0288659851	8.281889e-01
CYCLES_BETWEEN_PET1_PET2	ALT_PRE	-0.0284663224	8.305323e-01
CYCLES_BETWEEN_PET1_PET2	BMLR_pre	0.0276846049	8.351201e-01
HB_PRE	AST_PRE	0.0270412494	8.389000e-01
CYCLES_BETWEEN_PET1_PET2	BGL_PRE_PET1	0.0267492029	8.406170e-01
AGE	OVERALL_TIME	0.0260430419	8.447718e-01
ESTIMATED_SPLEE_VOL_PRE_PET1	TIME_BETWEE_PET	-0.0251923091	8.497828e-01
LDH_PRE	BW_PRE	-0.0250080395	8.508690e-01
BGL_PRE_PET1	BMLR_pre	0.0233252008	8.608010e-01
K_PRE	BM_UPT_PRE_PET1	0.0225119869	8.656083e-01
CRP_PRE	TIME_BETWEE_PET	0.0220300779	8.684594e-01
LDH_PRE	eGFR_PRE	0.0219107204	8.691658e-01
LDH_PRE	BM_UPT_PRE_PET1	0.0217817151	8.699294e-01
dias	OVERALL_TIME	0.0217613539	8.700499e-01
ESTIMATED_SPLEE_VOL_PRE_PET1	SLR_pre	-0.0210126398	8.744842e-01
LDH_PRE	LIVER_PRE_PET1	0.0208004446	8.757417e-01
AGE	WBC_PRE	-0.0202174016	8.791983e-01
WBC_PRE	OVERALL_TIME	0.0201786885	8.794279e-01
AGE	TIME_BETWEE_PET	0.0201627835	8.795222e-01
CYCLES_BETWEEN_PET1_PET2	K_PRE	0.0201604028	8.795363e-01
LIVER_PRE_PET1	OVERALL_TIME	0.0192155863	8.851428e-01
PLT_PRE	BGL_PRE_PET1	0.0190707459	8.860028e-01
HB_PRE	BGL_PRE_PET1	-0.0164418855	9.016331e-01
AGE	SPLEEN_FDG_UPT_PRE_PET1	0.0163761739	9.020243e-01
CRP_PRE	BW_PRE	-0.0159096930	9.048020e-01
CYCLES_BETWEEN_PET1_PET2	PLT_PRE	0.0156628287	9.062725e-01
RBC_PRE	SLR_pre	0.0155772800	9.067821e-01
ESTIMATED_SPLEE_VOL_PRE_PET1	BMLR_pre	0.0149624478	9.104460e-01
AGE	BMI	0.0149507110	9.105160e-01
ALBUMIN_PRE	BMI	-0.0149378086	9.105929e-01
RBC_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	-0.0146706412	9.121856e-01
K_PRE	OVERALL_TIME	-0.0146254440	9.124551e-01
BM_UPT_PRE_PET1	BMLR_pre	0.0142347717	9.147848e-01
ALBUMIN_PRE	BW_PRE	0.0140173944	9.160814e-01
AGE	LDH_PRE	-0.0138864558	9.168625e-01
RBC_PRE	ALT_PRE	0.0128737177	9.229066e-01
CYCLES_BETWEEN_PET1_PET2	BM_UPT_PRE_PET1	0.0126146201	9.244536e-01
eGFR_PRE	TIME_BETWEE_PET	0.0125826110	9.246447e-01
dias	BMLR_pre	0.0121821981	9.270361e-01
CRP_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.0110296813	9.339228e-01
WBC_PRE	BMLR_pre	-0.0108128644	9.352189e-01
BGL_PRE_PET1	BM_UPT_PRE_PET1	-0.0094197512	9.435506e-01
eGFR_PRE	OVERALL_TIME	-0.0088909703	9.467147e-01
SPLEEN_FDG_UPT_PRE_PET1	TIME_BETWEE_PET	0.0087914839	9.473101e-01
PLT_PRE	SLR_pre	-0.0084464643	9.493751e-01
CRP_PRE	AST_PRE	0.0083939009	9.496898e-01
BW_PRE	BMLR_pre	-0.0080805424	9.515657e-01
HB_PRE	SLR_pre	0.0076297944	9.542644e-01
HB_PRE	BMLR_pre	0.0047647585	9.714291e-01
ALBUMIN_PRE	ESTIMATED_SPLEE_VOL_PRE_PET1	0.0044725728	9.731805e-01
RBC_PRE	BMLR_pre	-0.0041498626	9.751150e-01
RBC_PRE	AST_PRE	0.0041209393	9.752884e-01
LIVER_PRE_PET1	SLR_pre	-0.0031858769	9.808944e-01
AST_PRE	SLR_pre	-0.0028350139	9.829982e-01
AGE	HB_PRE	-0.0024144349	9.855201e-01
PLT_PRE	CRP_PRE	-0.0023835990	9.857050e-01
HB_PRE	ALT_PRE	-0.0023532164	9.858872e-01
eGFR_PRE	SLR_pre	0.0017244151	9.896580e-01
BMI	SPLEEN_FDG_UPT_PRE_PET1	0.0016663988	9.900060e-01
HB_PRE	OVERALL_TIME	-0.0014041042	9.915790e-01
LDH_PRE	BMI	-0.0009792459	9.941270e-01
ESTIMATED_SPLEE_VOL_PRE_PET1	OVERALL_TIME	0.0008773215	9.947383e-01
ALBUMIN_PRE	eGFR_PRE	0.0005554662	9.966686e-01
ALBUMIN_PRE	SPLEEN_FDG_UPT_PRE_PET1	0.0003216845	9.980707e-01

Descripcion variables cualitativas de entrada

Tabla de frecuencias de las variables cualitativas

# Todas la tablas de frecuencias:
all_var <- univar_category(datos) 

all_var

$STATUS

A data.frame: 2 × 3

STATUS	n	rate
<fct>	<int>	<dbl>
ALIVE	38	0.6440678
DEATH	21	0.3559322

$GENDER

A data.frame: 2 × 3

GENDER	n	rate
<fct>	<int>	<dbl>
FEMALE	20	0.3389831
MALE	39	0.6610169

$TNM_STAGE

A data.frame: 4 × 3

TNM_STAGE	n	rate
<fct>	<int>	<dbl>
I	6	0.1016949
II	15	0.2542373
III	26	0.4406780
IV	12	0.2033898

$DIAGNOSTIC

A data.frame: 10 × 3

DIAGNOSTIC	n	rate
<fct>	<int>	<dbl>
EWING SARCOMA	2	0.03389831
GASTRIC CANCER	3	0.05084746
GINECOLOGICAL	7	0.11864407
HEAD AND NECK	10	0.16949153
LUNG CANCER	19	0.32203390
MELANOMA	5	0.08474576
PANCREAS CANCER	7	0.11864407
RENAL CANCER	4	0.06779661
SARCOMA	1	0.01694915
UROTHELIAL CARCINOMA	1	0.01694915

$TREATMENT

A data.frame: 2 × 3

TREATMENT	n	rate
<fct>	<int>	<dbl>
CHEMO	37	0.6271186
ICI	22	0.3728814

$ECOGPS

A data.frame: 5 × 3

ECOGPS	n	rate
<fct>	<int>	<dbl>
ASYMTOMATIC	26	0.44067797
BEDBOUND	1	0.01694915
SYMPTOMATIC >50 % IN THE BED	1	0.01694915
SYMPTOMATIC BUT AMBULATORY	17	0.28813559
SYMPTOMATIC,<50% IN BED DURING THE DAY	14	0.23728814

$COMORBIDITIES

A data.frame: 6 × 3

COMORBIDITIES	n	rate
<fct>	<int>	<dbl>
ATEROESCLEROSIS	3	0.05084746
CANCER RRECURRENCE	8	0.13559322
CHRONIC INFLAMMATION	9	0.15254237
DIABETES MELLITUS	13	0.22033898
HYPERLIPIDEMIA	12	0.20338983
HYPERTENSION	14	0.23728814

$CTCNCI

A data.frame: 5 × 3

CTCNCI	n	rate
<fct>	<int>	<dbl>
HIGHT SEVERE	4	0.06779661
MODERATE	11	0.18644068
NO SIDE EFFECTS	28	0.47457627
SEVERE	12	0.20338983
SLIGTHLY SIDE EFFCTS	4	0.06779661

$ACTION_TAKEN_

A data.frame: 7 × 3

ACTION_TAKEN_	n	rate
<fct>	<int>	<dbl>
ADDED OTHER TREATMEN	3	0.05084746
ADDED STEROIDS	1	0.01694915
DOSE NOT CHANGED	21	0.35593220
DOSE REDUCED	17	0.28813559
DRUG INTERRUPTED	9	0.15254237
DRUG WIHDRAWN	1	0.01694915
UNKNOW	7	0.11864407

Graficos de barras de las variables cualitativas

# Diagrama de barras por separado:
plot(all_var)

Todos los diagramas de barra juntos

# Todos los diagramas de barra juntos:
plot_bar_category(datos)

Tabla de frecuencias + diagrama de barras + diagrama de pastel de todas las cualitativas

# Tabla de frecuencias + diagrama de barras + diagrama de sectores en una iteración:

vec_var_cual <- c('GENDER','TNM_STAGE','DIAGNOSTIC','TREATMENT','ECOGPS','COMORBIDITIES','CTCNCI','ACTION_TAKEN_')

for (i in vec_var_cual) {
    
    # 0. Seleccionar la variable con el contador i:
    #***************************************************************    
    var_i = i
    
    print("********************************************")
    print(var_i)
    print("********************************************")

    # 1. Creo la tabla de frecuencias relativas y absolutas:
    #***************************************************************

    # Tabla de frecuencias absolutas:
    tabla_frec = data.frame(t(table(datos[,var_i])))[,2:3]
    colnames(tabla_frec)<-c("Grupos","Frec_Absoluta")

    # Tabla de frecuencias relativas:
    tabla_frec$Frec_Relativa <- tabla_frec$Frec_Absoluta/sum(tabla_frec$Frec_Absoluta)
    print(tabla_frec)


    # 3. Crear el diagrama de sectores
    #***************************************************************
    # Create Data
    data <- data.frame(
      group=tabla_frec$Grupos,
      value=tabla_frec$Frec_Relativa
    )

    data <- data %>% 
      arrange(desc(group)) %>%
      mutate(prop = value / sum(data$value) *100) %>%
      mutate(ypos = cumsum(prop)- 0.5*prop )


    p2 <- ggplot(data, aes(x="", y=prop, fill=group)) +
      geom_bar(stat="identity", width=1, color="white") +
      coord_polar("y", start=0) +
      theme_void() + 
      theme(legend.position="none") +

      geom_text(aes(y = ypos, label = group), color = "white", size=6) +
      scale_fill_brewer(palette="Set1")

    
    
    # 2. Crear el barplot frecuencia Absoluta
    #***************************************************************
    p1 <- ggplot(data=tabla_frec, aes(x=Grupos, y=Frec_Absoluta)) +
      geom_bar(stat="identity", fill="steelblue")+
      geom_text(aes(label=Frec_Relativa), vjust=1.6, color="black", size=5)+
      theme_minimal()

   
      grid.arrange(p1, p2,
               nrow=2, ncol = 1,
               top = textGrob(var_i,gp=gpar(fontsize=16,font=3)))    
    
    
}

[1] "********************************************"
[1] "GENDER"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1 FEMALE            20     0.3389831
2   MALE            39     0.6610169
[1] "********************************************"
[1] "TNM_STAGE"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1      I             6     0.1016949
2     II            15     0.2542373
3    III            26     0.4406780
4     IV            12     0.2033898

[1] "********************************************"
[1] "DIAGNOSTIC"
[1] "********************************************"
                 Grupos Frec_Absoluta Frec_Relativa
1         EWING SARCOMA             2    0.03389831
2        GASTRIC CANCER             3    0.05084746
3         GINECOLOGICAL             7    0.11864407
4         HEAD AND NECK            10    0.16949153
5           LUNG CANCER            19    0.32203390
6              MELANOMA             5    0.08474576
7       PANCREAS CANCER             7    0.11864407
8          RENAL CANCER             4    0.06779661
9               SARCOMA             1    0.01694915
10 UROTHELIAL CARCINOMA             1    0.01694915

[1] "********************************************"
[1] "TREATMENT"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1  CHEMO            37     0.6271186
2    ICI            22     0.3728814

[1] "********************************************"
[1] "ECOGPS"
[1] "********************************************"
                                  Grupos Frec_Absoluta Frec_Relativa
1                            ASYMTOMATIC            26    0.44067797
2                               BEDBOUND             1    0.01694915
3           SYMPTOMATIC >50 % IN THE BED             1    0.01694915
4             SYMPTOMATIC BUT AMBULATORY            17    0.28813559
5 SYMPTOMATIC,<50% IN BED DURING THE DAY            14    0.23728814

[1] "********************************************"
[1] "COMORBIDITIES"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1      ATEROESCLEROSIS             3    0.05084746
2   CANCER RRECURRENCE             8    0.13559322
3 CHRONIC INFLAMMATION             9    0.15254237
4    DIABETES MELLITUS            13    0.22033898
5       HYPERLIPIDEMIA            12    0.20338983
6         HYPERTENSION            14    0.23728814

[1] "********************************************"
[1] "CTCNCI"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1         HIGHT SEVERE             4    0.06779661
2             MODERATE            11    0.18644068
3      NO SIDE EFFECTS            28    0.47457627
4               SEVERE            12    0.20338983
5 SLIGTHLY SIDE EFFCTS             4    0.06779661

[1] "********************************************"
[1] "ACTION_TAKEN_"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1 ADDED OTHER TREATMEN             3    0.05084746
2       ADDED STEROIDS             1    0.01694915
3     DOSE NOT CHANGED            21    0.35593220
4         DOSE REDUCED            17    0.28813559
5     DRUG INTERRUPTED             9    0.15254237
6        DRUG WIHDRAWN             1    0.01694915
7               UNKNOW             7    0.11864407

EXPLORACION MULTIVARIADA DEL DATASET

Variables numericas vs salida

Diagrama de dispersion por la salida

# Diagrama de dispersion por pareja

options(repr.plot.width=8, repr.plot.height=5)
var_respuesta <- as.name(respuesta)

for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  r <- list()
  
    r[[1]] <- ggplot(datos, aes(x=!!name_i, y=!!var_respuesta)) +
      geom_point() +
      geom_smooth(method=lm ,formula = y ~ x, color="red", fill="#69b3a2", se=TRUE)  +
    theme_minimal()
  
  grid.arrange(r[[1]],
               nrow=1, ncol = 1,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Orden de importancia de las variables cualitativas en el dataset

# Mutual informacion variables cualitativas:
inf_gain <- information.gain(STATUS~., data = datos)
inf_gain <- inf_gain %>%
  arrange(desc(attr_importance))
inf_gain

A data.frame: 33 × 1

	attr_importance
	<dbl>
ACTION_TAKEN_	1.327590e-01
DIAGNOSTIC	1.265327e-01
TNM_STAGE	8.123244e-02
ECOGPS	5.316641e-02
COMORBIDITIES	5.272323e-02
CTCNCI	2.358443e-02
GENDER	1.294311e-02
TREATMENT	7.687645e-05
CYCLES_BETWEEN_PET1_PET2	0.000000e+00
AGE	0.000000e+00
WBC_PRE	0.000000e+00
RBC_PRE	0.000000e+00
HB_PRE	0.000000e+00
PLT_PRE	0.000000e+00
CRP_PRE	0.000000e+00
ALBUMIN_PRE	0.000000e+00
LDH_PRE	0.000000e+00
eGFR_PRE	0.000000e+00
AST_PRE	0.000000e+00
ALT_PRE	0.000000e+00
K_PRE	0.000000e+00
BGL_PRE_PET1	0.000000e+00
BMI	0.000000e+00
BW_PRE	0.000000e+00
SPLEEN_FDG_UPT_PRE_PET1	0.000000e+00
BM_UPT_PRE_PET1	0.000000e+00
LIVER_PRE_PET1	0.000000e+00
ESTIMATED_SPLEE_VOL_PRE_PET1	0.000000e+00
TIME_BETWEE_PET	0.000000e+00
dias	0.000000e+00
SLR_pre	0.000000e+00
BMLR_pre	0.000000e+00
OVERALL_TIME	0.000000e+00

Estadisticos numericos variables numericos vs salida

# 6. DESCRIPCION MULTIVARIADA: las variables en función de la respuesta
name_y <- as.name(respuesta[1])

# Descripciones de las variables cuantiativas vs salida
descripcion_num <- datos %>%
  group_by(!!name_y)%>%
  dlookr::describe()
descripcion_num

A tibble: 50 × 27

described_variables	STATUS	n	na	mean	sd	se_mean	IQR	skewness	kurtosis	p00	p01	p05	p10	p20	p25	p30	p40	p50	p60	p70	p75	p80	p90	p95	p99	p100
<chr>	<fct>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
AGE	ALIVE	38	0	68.5526316	8.1562358	1.32311616	12.2500000	-0.35796368	-0.48367838	52.0000000	52.7400000	54.0000000	55.7000000	60.4000000	62.5000000	65.0000000	68.0000000	70.0000000	72.0000000	74.000000	74.750000	75.600000	76.300000	79.000000	83.410000	86.000000
AGE	DEATH	21	0	70.9047619	9.4228698	2.05623878	14.0000000	-0.14495828	-0.82993240	53.0000000	53.6000000	56.0000000	60.0000000	63.0000000	64.0000000	67.0000000	68.0000000	70.0000000	74.0000000	77.000000	78.000000	79.000000	83.000000	83.000000	86.200000	87.000000
ALBUMIN_PRE	ALIVE	38	0	3.7957895	0.5630834	0.09134418	0.5225000	-1.11593692	1.04212745	2.1300000	2.3224000	2.7180000	2.9170000	3.3880000	3.6250000	3.7030000	3.8160000	3.9500000	3.9920000	4.100000	4.147500	4.226000	4.338000	4.479000	4.586700	4.620000
ALBUMIN_PRE	DEATH	21	0	3.8304762	0.5007642	0.10927570	0.5000000	-1.80020192	5.06822812	2.1800000	2.3860000	3.2100000	3.4300000	3.6800000	3.6900000	3.7000000	3.7500000	3.8100000	3.9500000	4.180000	4.190000	4.190000	4.300000	4.350000	4.446000	4.470000
ALT_PRE	ALIVE	38	0	22.2657895	15.3574927	2.49131429	15.3500000	3.15067011	14.40321439	3.7000000	4.8100000	7.8050000	9.8700000	13.7800000	14.0750000	14.5100000	15.9800000	17.6000000	20.8800000	27.810000	29.425000	29.800000	33.220000	36.775000	75.817000	96.500000
ALT_PRE	DEATH	21	0	35.3419048	60.6480327	13.23448575	12.8000000	3.78245648	15.03863546	3.2000000	4.3600000	9.0000000	9.5000000	12.7000000	14.2000000	14.9000000	16.3000000	19.5000000	20.5000000	23.100000	27.000000	27.180000	34.800000	114.800000	248.160000	281.500000
AST_PRE	ALIVE	38	0	24.4473684	18.2988810	2.96847049	7.6750000	4.50577740	23.78498297	6.1000000	8.8380000	13.5850000	14.9600000	16.2000000	17.4750000	17.9000000	19.0200000	20.3000000	21.6400000	23.190000	25.150000	25.900000	34.420000	44.315000	95.239000	123.100000
AST_PRE	DEATH	21	0	35.9290476	52.7780733	11.51711980	10.6000000	3.84068125	15.65472906	11.9000000	12.1200000	13.0000000	13.0000000	13.7000000	14.9000000	15.7000000	21.1000000	21.9000000	23.4000000	24.600000	25.500000	27.800000	53.300000	94.200000	220.600000	252.200000
BGL_PRE_PET1	ALIVE	38	0	114.2105263	38.9498346	6.31849752	27.5000000	2.08022037	4.92887622	74.0000000	75.1100000	79.5500000	83.7000000	88.2000000	90.7500000	93.2000000	98.0000000	100.0000000	108.6000000	115.600000	118.250000	126.000000	167.100000	184.000000	239.430000	262.000000
BGL_PRE_PET1	DEATH	21	0	106.6190476	27.7443259	6.05430826	15.0000000	1.96982468	4.59209992	75.0000000	76.2000000	81.0000000	83.0000000	93.0000000	94.0000000	94.0000000	95.0000000	100.0000000	101.0000000	104.000000	109.000000	116.000000	140.000000	150.000000	186.800000	196.000000
BM_UPT_PRE_PET1	ALIVE	38	0	2.4044737	0.4882993	0.07921261	0.5950000	-0.10892825	0.16091481	1.3100000	1.3433000	1.5615000	1.8370000	2.0120000	2.1025000	2.2000000	2.3000000	2.4350000	2.5500000	2.636000	2.697500	2.768000	2.924000	3.216500	3.429700	3.500000
BM_UPT_PRE_PET1	DEATH	21	0	2.3709524	0.4132905	0.09018738	0.5400000	-0.06965851	-0.47167636	1.5600000	1.5980000	1.7500000	1.8800000	2.0000000	2.0600000	2.1900000	2.3100000	2.4100000	2.4500000	2.530000	2.600000	2.650000	2.900000	3.040000	3.080000	3.090000
BMI	ALIVE	38	0	22.1357895	4.2896479	0.69587278	5.3525000	0.88974456	1.40256997	15.4300000	15.7297000	16.4355000	17.1050000	18.2240000	18.8325000	20.1230000	20.8080000	21.8250000	22.8320000	23.579000	24.185000	25.708000	26.914000	29.177500	33.497500	35.810000
BMI	DEATH	21	0	20.4747619	3.3661545	0.73455514	5.6200000	-0.05915208	-1.07758686	14.0800000	14.4980000	16.1700000	16.6800000	17.3000000	17.8900000	18.0700000	19.2600000	20.0700000	21.7900000	23.140000	23.510000	23.760000	24.460000	25.210000	25.666000	25.780000
BMLR_pre	ALIVE	38	0	0.9706065	0.2563308	0.04158235	0.2997767	1.15745006	3.55551554	0.4946996	0.5251850	0.6619485	0.6952792	0.7390075	0.7878715	0.8561207	0.9058442	0.9888729	1.0092731	1.069046	1.087648	1.126667	1.193460	1.290253	1.740426	1.895425
BMLR_pre	DEATH	21	0	1.0031606	0.2179211	0.04755429	0.2222760	1.45432156	2.49319478	0.7062500	0.7211562	0.7807808	0.7849829	0.8756614	0.8787879	0.8914956	0.9094488	0.9552239	0.9930314	1.062271	1.101064	1.115385	1.181373	1.458333	1.584596	1.616162
BW_PRE	ALIVE	38	0	57.2894737	12.6083153	2.04533883	15.7000000	0.55893823	0.76960150	33.5000000	35.6090000	39.8800000	41.4900000	46.0000000	48.5500000	50.1800000	54.8400000	57.4000000	59.1800000	63.470000	64.250000	67.220000	69.650000	78.275000	89.094000	94.200000
BW_PRE	DEATH	21	0	52.7561905	12.9480464	2.82549536	14.7000000	-1.06771876	2.97454411	14.0800000	19.0840000	39.1000000	41.5000000	45.4000000	45.7000000	47.3000000	50.7000000	51.8000000	55.4000000	60.400000	60.400000	62.000000	65.900000	67.800000	74.200000	75.800000
CRP_PRE	ALIVE	38	0	1.1097368	1.9033392	0.30876238	0.5925000	2.03462033	3.33693226	0.0100000	0.0137000	0.0200000	0.0200000	0.0540000	0.0600000	0.0910000	0.1380000	0.1800000	0.3100000	0.599000	0.652500	1.718000	3.970000	4.983500	7.012100	7.260000
CRP_PRE	DEATH	21	0	1.8019048	4.1777011	0.91164911	0.5700000	3.64787518	14.39621273	0.0200000	0.0200000	0.0200000	0.0400000	0.1800000	0.2000000	0.2100000	0.2600000	0.3500000	0.3900000	0.550000	0.770000	1.290000	3.870000	6.370000	16.178000	18.630000
CYCLES_BETWEEN_PET1_PET2	ALIVE	38	0	7.3684211	8.8330493	1.43290981	3.7500000	3.23904673	12.32718502	2.0000000	2.0000000	2.0000000	2.0000000	3.0000000	3.0000000	3.0000000	4.0000000	4.0000000	5.0000000	6.000000	6.750000	11.200000	14.000000	21.350000	40.970000	48.000000
CYCLES_BETWEEN_PET1_PET2	DEATH	21	0	6.5238095	4.0201872	0.87727676	4.0000000	1.58544523	2.70180500	2.0000000	2.0000000	2.0000000	3.0000000	4.0000000	4.0000000	4.0000000	5.0000000	6.0000000	6.0000000	7.000000	8.000000	9.000000	10.000000	15.000000	17.400000	18.000000
dias	ALIVE	38	0	46.8421053	137.8935207	22.36928289	23.7500000	4.06563184	17.78192579	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	2.0000000	14.900000	23.750000	30.400000	64.200000	319.650000	597.360000	735.000000
dias	DEATH	21	0	75.8095238	127.5623844	27.83639439	86.0000000	1.95190402	3.08842106	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	3.0000000	7.0000000	33.0000000	52.000000	86.000000	114.000000	233.000000	364.000000	425.600000	441.000000
eGFR_PRE	ALIVE	38	0	68.0105263	12.2979407	1.99498942	17.3000000	0.30544577	-0.06003580	41.0000000	44.9590000	51.8700000	54.1900000	57.3800000	58.7750000	60.2000000	63.1000000	66.7000000	72.2200000	73.950000	76.075000	78.000000	81.610000	89.950000	95.586000	96.400000
eGFR_PRE	DEATH	21	0	76.9476190	35.4929235	7.74519089	26.9000000	2.84131782	10.35403667	39.8000000	40.9800000	45.7000000	46.9000000	53.8000000	57.6000000	62.5000000	69.2000000	71.3000000	74.8000000	77.200000	84.500000	85.100000	96.600000	114.000000	191.280000	210.600000
ESTIMATED_SPLEE_VOL_PRE_PET1	ALIVE	38	0	143.3026316	61.1323619	9.91697862	72.8750000	0.98965582	0.68598500	50.8000000	53.8340000	76.0000000	81.7100000	96.5800000	98.2500000	104.1100000	114.7600000	125.1500000	145.4800000	164.270000	171.125000	181.620000	234.270000	260.210000	296.113000	316.500000
ESTIMATED_SPLEE_VOL_PRE_PET1	DEATH	21	0	149.1523810	54.4306680	11.87774554	57.0000000	0.90601407	1.04733786	56.9000000	63.6400000	90.6000000	92.5000000	112.0000000	116.0000000	117.8000000	138.6000000	145.3000000	148.4000000	160.000000	173.000000	174.700000	210.300000	262.600000	277.320000	281.000000
HB_PRE	ALIVE	38	0	12.0042105	2.1079831	0.34196001	2.9500000	-0.13880298	-0.43535197	7.3000000	7.6700000	8.5210000	9.3300000	10.3200000	10.7000000	11.1300000	11.5800000	11.7500000	12.4200000	13.270000	13.650000	13.900000	14.680000	15.430000	15.663000	15.700000
HB_PRE	DEATH	21	0	12.5333333	2.0222100	0.44128240	2.2000000	-0.52274310	-0.02985859	8.2000000	8.3200000	8.8000000	9.6000000	11.3000000	11.6000000	12.0000000	12.2000000	12.7000000	13.1000000	13.400000	13.800000	14.600000	15.000000	15.000000	15.640000	15.800000
K_PRE	ALIVE	38	0	4.2657895	0.3521335	0.05712360	0.6025000	0.34365126	-0.54862710	3.6900000	3.7233000	3.8140000	3.8300000	3.9200000	3.9400000	4.0220000	4.1240000	4.2550000	4.4060000	4.498000	4.542500	4.556000	4.690000	4.768000	5.046400	5.150000
K_PRE	DEATH	21	0	4.2395238	0.4226521	0.09223024	0.5400000	-0.63083821	1.14258830	3.1100000	3.2380000	3.7500000	3.9000000	3.9700000	3.9700000	3.9800000	4.0900000	4.2800000	4.3700000	4.410000	4.510000	4.670000	4.710000	4.810000	4.906000	4.930000
LDH_PRE	ALIVE	38	0	198.8105263	100.7466820	16.34326993	43.2500000	2.68225454	7.28744364	77.8000000	84.5340000	120.6500000	132.1000000	145.4000000	157.2500000	159.2000000	174.0000000	178.0000000	184.0000000	197.700000	200.500000	205.000000	228.000000	484.650000	543.770000	573.000000
LDH_PRE	DEATH	21	0	231.8095238	164.2411699	35.84036160	66.0000000	2.70732400	6.72154960	111.0000000	113.8000000	125.0000000	139.0000000	142.0000000	150.0000000	151.0000000	172.0000000	204.0000000	210.0000000	214.000000	216.000000	221.000000	253.000000	670.000000	734.800000	751.000000
LIVER_PRE_PET1	ALIVE	38	0	2.4084211	0.4367603	0.07085188	0.5875000	0.48581409	1.61826242	1.4900000	1.5048000	1.8275000	1.9710000	2.0220000	2.1100000	2.2240000	2.2880000	2.3600000	2.5120000	2.599000	2.697500	2.772000	2.839000	2.954000	3.524700	3.780000
LIVER_PRE_PET1	DEATH	21	0	2.6519048	0.4027483	0.08788688	0.4200000	0.01403399	-0.30052396	1.8900000	1.9280000	2.0800000	2.1100000	2.2500000	2.4400000	2.5100000	2.6000000	2.6900000	2.7300000	2.820000	2.860000	2.870000	3.200000	3.330000	3.394000	3.410000
OVERALL_TIME	ALIVE	38	0	17.7631579	16.2537883	2.63671263	16.5000000	1.49609275	1.96731456	1.0000000	1.0000000	1.8500000	2.7000000	4.4000000	6.0000000	7.2000000	11.0000000	14.5000000	15.2000000	20.000000	22.500000	25.800000	41.000000	51.950000	64.260000	65.000000
OVERALL_TIME	DEATH	21	0	21.5238095	15.1480000	3.30556461	17.0000000	1.30493144	1.91970514	1.0000000	1.6000000	4.0000000	8.0000000	12.0000000	12.0000000	13.0000000	16.0000000	17.0000000	21.0000000	23.000000	29.000000	30.000000	42.000000	47.000000	60.600000	64.000000
PLT_PRE	ALIVE	38	0	253.0789474	114.1263836	18.51374414	130.2500000	1.24759679	1.21957508	106.0000000	111.5500000	125.2500000	142.8000000	162.2000000	176.5000000	187.2000000	206.0000000	217.5000000	244.6000000	280.700000	306.750000	335.800000	388.800000	498.000000	556.580000	581.000000
PLT_PRE	DEATH	21	0	219.4285714	93.1206591	20.32059376	84.0000000	1.42319105	2.24589115	78.0000000	88.8000000	132.0000000	140.0000000	158.0000000	163.0000000	170.0000000	193.0000000	200.0000000	210.0000000	222.000000	247.000000	262.000000	315.000000	442.000000	455.600000	459.000000
RBC_PRE	ALIVE	38	0	4.0513158	0.6786440	0.11009059	0.7425000	-0.46519364	0.69869643	2.2500000	2.3980000	2.8285000	3.2540000	3.6220000	3.6650000	3.7620000	4.0620000	4.1200000	4.3000000	4.358000	4.407500	4.422000	4.883000	5.164500	5.341200	5.430000
RBC_PRE	DEATH	21	0	4.1314286	0.6181366	0.13488846	0.6900000	-0.54623453	0.36384720	2.7200000	2.7740000	2.9900000	3.3400000	3.8500000	3.8600000	3.9400000	3.9800000	4.2000000	4.2700000	4.460000	4.550000	4.690000	4.800000	4.950000	5.158000	5.210000
SLR_pre	ALIVE	38	0	0.9814276	0.1646818	0.02671492	0.2106722	0.49345198	-0.18204133	0.7102473	0.7242998	0.7625899	0.7791925	0.8318182	0.8575311	0.8819217	0.9110861	0.9977876	1.0146910	1.045247	1.068203	1.087344	1.177965	1.330324	1.339409	1.339869
SLR_pre	DEATH	21	0	0.9524327	0.2066883	0.04510309	0.1964809	1.22516208	1.89503811	0.6486486	0.6718290	0.7645503	0.7684564	0.7806691	0.8035191	0.8062500	0.8717949	0.9166667	0.9722222	0.988189	1.000000	1.117647	1.166667	1.271795	1.484146	1.537234
SPLEEN_FDG_UPT_PRE_PET1	ALIVE	38	0	2.3552632	0.2624596	0.04257658	0.2875000	0.25112213	-0.71619335	1.9600000	1.9674000	1.9800000	2.0070000	2.0700000	2.1925000	2.2020000	2.2760000	2.3550000	2.4060000	2.477000	2.480000	2.618000	2.686000	2.779500	2.890000	2.890000
SPLEEN_FDG_UPT_PRE_PET1	DEATH	21	0	2.3723810	0.2509363	0.05475880	0.3800000	0.66043156	-0.94781786	2.1100000	2.1100000	2.1100000	2.1300000	2.1500000	2.1500000	2.1500000	2.2000000	2.2900000	2.4100000	2.510000	2.530000	2.580000	2.740000	2.810000	2.842000	2.850000
TIME_BETWEE_PET	ALIVE	38	0	10.5789474	8.9703162	1.45517743	11.5000000	1.40520467	1.58539181	1.0000000	1.3700000	2.0000000	2.7000000	4.0000000	4.0000000	5.0000000	6.0000000	7.5000000	9.0000000	11.800000	15.500000	18.200000	22.200000	26.200000	36.150000	38.000000
TIME_BETWEE_PET	DEATH	21	0	7.9047619	3.9484777	0.86162847	7.0000000	-0.18573481	-0.95771678	1.0000000	1.2000000	2.0000000	3.0000000	4.0000000	4.0000000	5.0000000	8.0000000	9.0000000	9.0000000	10.000000	11.000000	11.000000	12.000000	13.000000	14.600000	15.000000
WBC_PRE	ALIVE	38	0	6.0121053	2.6179496	0.42468751	2.9650000	0.39233540	0.44660813	1.5200000	1.5755000	1.7125000	2.3800000	4.1540000	4.4425000	4.5700000	5.3040000	5.9850000	6.6340000	7.253000	7.407500	7.790000	9.058000	9.960500	12.400900	13.300000
WBC_PRE	DEATH	21	0	5.7719048	2.5538023	0.55728535	3.2700000	1.16147740	2.53426202	1.5900000	1.9620000	3.4500000	3.4500000	3.7500000	3.9700000	4.0800000	4.6900000	5.1400000	6.5500000	7.080000	7.240000	7.430000	7.950000	8.660000	12.388000	13.320000

Graficos descriptivos variables numericas respecto a la salida

options(repr.plot.width=16, repr.plot.height=8)


# boxplot + diagrama de error + histograma de densidad + violin plot
for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  name_y <- as.name(respuesta[1])
  
  r <- list()
  
  # Histograma de densidad
  r[[1]]<-ggplot(datos, aes(x=!!name_i, fill=!!name_y)) +
    geom_density(alpha=0.4)+
    theme_light() +
    #theme(legend.position = "none") +
    xlab("") +
    ylab("Densidad de Frecuencia")
  
  # Boxplot
  r[[2]]<-ggplot(data=datos, aes(y=!!name_i, x=!!name_y)) +
    geom_boxplot(size = 0.4) +
    theme_light() +
    theme(legend.position = "none")+
    xlab("")
  
  # Violin plot con diagramas de error
  r[[3]]<-ggerrorplot(data = datos, x = respuesta, y = var_num[i], 
                      desc_stat = "mean_ci",
                      error.plot = "errorbar",
                      add = c("violin","mean"))+
    theme_light() +
    theme(legend.position = "none") +
    xlab("") +
    ylab("") +
    #ggtitle("Comparacion pvut") +
    stat_compare_means(comparisons = c("0","1"))+ # Add pairwise comparisons p-value
    stat_compare_means(label.y = 1.05*max(datos[,var_num[i]]))                  # Add global p-value
  
  grid.arrange(r[[1]], r[[2]], r[[3]],
               nrow=3, ncol = 1,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Variables cualitativas vs salida

Todos los graficos de barras de las variables cualitativas segun la salida

# Todos los graficos de barras de cualitativas vs respuesta
name_y <- as.name(respuesta[1])

datos %>%
  group_by(!!name_y) %>%
  plot_bar_category()

Graficos descriptivos variables cualitativas vs salida(Mosaic plot + Chi cuadrado)

# Mosaic plot y chi cuadrado:
r <- list()
for (i in 1:length(var_cual)) {
    # Variable de salida o respuesta
    name_y <- as.name(respuesta[1])

    # Variable de entrada
    name_i <- as.name(var_cual[i])


    # Tabla de contingencias de una variable con la target
    categ <- target_by(datos, !!name_y)
    cat_cat <- relate(categ, !!name_i)
    r[[i]]<-as.data.frame.matrix(cat_cat)
    
    # Chi cuadrado
    print(summary(cat_cat))
    
    # Mosaico de una variable con la target
    grid.arrange(plot(cat_cat),nrow=1, ncol = 1)
    
}
r

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 1.4811, df = 1, p-value = 0.2236
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 9.495, df = 3, p-value = 0.02338
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 12.737, df = 9, p-value = 0.1749
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 0.009083, df = 1, p-value = 0.9241

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 5.748, df = 4, p-value = 0.2188
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 5.938, df = 5, p-value = 0.3123
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 2.7546, df = 4, p-value = 0.5997
	Chi-squared approximation may be incorrect

Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 13.937, df = 6, p-value = 0.03035
	Chi-squared approximation may be incorrect

1. A data.frame: 2 × 2

   	FEMALE	MALE
   	<int>	<int>
   ALIVE	15	23
   DEATH	5	16

2. A data.frame: 2 × 4

   	I	II	III	IV
   	<int>	<int>	<int>	<int>
   ALIVE	1	8	19	10
   DEATH	5	7	7	2

3. A data.frame: 2 × 10

   	EWING SARCOMA	GASTRIC CANCER	GINECOLOGICAL	HEAD AND NECK	LUNG CANCER	MELANOMA	PANCREAS CANCER	RENAL CANCER	SARCOMA	UROTHELIAL CARCINOMA
   	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
   ALIVE	2	0	6	6	14	2	4	3	0	1
   DEATH	0	3	1	4	5	3	3	1	1	0

4. A data.frame: 2 × 2

   	CHEMO	ICI
   	<int>	<int>
   ALIVE	24	14
   DEATH	13	8

5. A data.frame: 2 × 5

   	ASYMTOMATIC	BEDBOUND	SYMPTOMATIC >50 % IN THE BED	SYMPTOMATIC BUT AMBULATORY	SYMPTOMATIC,<50% IN BED DURING THE DAY
   	<int>	<int>	<int>	<int>	<int>
   ALIVE	19	0	1	8	10
   DEATH	7	1	0	9	4

6. A data.frame: 2 × 6

   	ATEROESCLEROSIS	CANCER RRECURRENCE	CHRONIC INFLAMMATION	DIABETES MELLITUS	HYPERLIPIDEMIA	HYPERTENSION
   	<int>	<int>	<int>	<int>	<int>	<int>
   ALIVE	2	3	5	9	7	12
   DEATH	1	5	4	4	5	2

7. A data.frame: 2 × 5

   	HIGHT SEVERE	MODERATE	NO SIDE EFFECTS	SEVERE	SLIGTHLY SIDE EFFCTS
   	<int>	<int>	<int>	<int>	<int>
   ALIVE	2	6	21	7	2
   DEATH	2	5	7	5	2

8. A data.frame: 2 × 7

   	ADDED OTHER TREATMEN	ADDED STEROIDS	DOSE NOT CHANGED	DOSE REDUCED	DRUG INTERRUPTED	DRUG WIHDRAWN	UNKNOW
   	<int>	<int>	<int>	<int>	<int>	<int>	<int>
   ALIVE	3	0	17	9	3	0	6
   DEATH	0	1	4	8	6	1	1

DEFINIMOS LOS DATAFRAMES DE ENTRADA Y SALIDA

# Voy a definir el data frame de entrada: X
X = datos[-1]  
head(X)

# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)

A data.frame: 6 × 33

	CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	WBC_PRE	RBC_PRE	HB_PRE	PLT_PRE	CRP_PRE	ALBUMIN_PRE	LDH_PRE	eGFR_PRE	AST_PRE	ALT_PRE	K_PRE	BGL_PRE_PET1	BMI	BW_PRE	SPLEEN_FDG_UPT_PRE_PET1	BM_UPT_PRE_PET1	LIVER_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	SLR_pre	BMLR_pre	OVERALL_TIME
	<dbl>	<fct>	<dbl>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<fct>	<fct>	<fct>	<fct>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	3	MALE	54	III	3.82	3.03	9.70	146	3.67	2.13	573.0	60.1	16.8	6.7	4.87	93	22.90	66.5	2.67	2.86	2.45	316.5	GINECOLOGICAL	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	25	0.8586957	1.0144928	2
2	2	FEMALE	79	IV	10.87	3.93	11.50	515	4.11	2.65	158.0	57.3	13.5	8.0	3.92	108	16.47	33.5	2.40	2.70	2.70	102.3	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DRUG INTERRUPTED	4	0	1.0099502	0.9303483	50
3	3	MALE	60	III	8.40	4.41	8.56	355	0.09	4.32	77.8	82.1	19.1	13.7	4.56	87	20.15	58.1	2.57	1.31	2.27	81.5	LUNG CANCER	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE REDUCED	4	14	0.9955752	1.0884956	2
4	3	MALE	76	II	4.66	3.55	11.70	178	0.10	3.70	195.0	65.7	17.7	15.3	3.83	135	22.83	56.7	2.25	2.46	2.26	224.4	RENAL CANCER	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	0.8811475	0.7172131	12
5	5	FEMALE	70	II	5.86	5.19	15.10	343	7.26	3.94	166.0	79.9	18.1	10.2	3.83	111	18.29	41.7	2.48	2.52	2.55	260.0	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	0.8269231	1.0192308	12
6	4	MALE	54	IV	2.95	4.45	14.50	167	0.25	4.16	135.0	76.3	14.4	14.0	4.55	98	27.18	80.4	2.01	1.40	2.83	198.4	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	HIGHT SEVERE	DOSE NOT CHANGED	6	363	1.1321586	0.5770925	18

1. ALIVE
2. ALIVE
3. ALIVE
4. ALIVE
5. ALIVE
6. ALIVE

Levels:

1. 'ALIVE'
2. 'DEATH'

FEATURE SELECTION (SELECCION DE CARACTERISTICAS)

1. Feature selection univariada ( ANOVA/Kruskall Wallis = Cuantitativa vs salida o Chi Cuadrado = Cualitativas vs salida, MI = Mutual information)

• Quitar las variables con MI = 0
• Quitar las variables con p-valores >25% = 0.25

1. Feature selection multivariado:

• Modelo logístico (caso binario) mejor modelo según el BIC. --> con pocos datos y pocas variables

Feature selection univariado: cuantitativas vs la salida

Wilcoxon rank test

Wilcoxon rank sum test es para comparar dos medianas. Casos donde la variable de salida es binaria.

## Multiple Wilcoxon rank sum tests - casos binarios

Wilk_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) wilcox.test(x ~ as.numeric(datos$STATUS))$p.value)))) %>%
  arrange(V1)
                                               
Wilk_p

A data.frame: 25 × 1

	V1
	<dbl>
LIVER_PRE_PET1	0.03661853
dias	0.10842987
OVERALL_TIME	0.16811048
BMI	0.20532924
CYCLES_BETWEEN_PET1_PET2	0.25040192
PLT_PRE	0.28165920
HB_PRE	0.28510675
CRP_PRE	0.29208464
SLR_pre	0.29241632
BW_PRE	0.35436014
AGE	0.36628834
LDH_PRE	0.36672313
eGFR_PRE	0.47616981
ESTIMATED_SPLEE_VOL_PRE_PET1	0.47620848
WBC_PRE	0.59038639
BGL_PRE_PET1	0.60106294
RBC_PRE	0.68642375
BMLR_pre	0.70397948
BM_UPT_PRE_PET1	0.73352128
TIME_BETWEE_PET	0.74493899
AST_PRE	0.84311765
SPLEEN_FDG_UPT_PRE_PET1	0.86792438
ALT_PRE	0.91803659
K_PRE	0.95580153
ALBUMIN_PRE	0.96841944

kruskall wallis test

## Kruskall wallis rank sum tests - todos los casos
Kruskal_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) kruskal.test(x ~ as.numeric(datos$STATUS))$p.value)))) %>%
  arrange(V1)
                                               
Kruskal_p

A data.frame: 25 × 1

	V1
	<dbl>
LIVER_PRE_PET1	0.03591323
dias	0.10657425
OVERALL_TIME	0.16567915
BMI	0.20251128
CYCLES_BETWEEN_PET1_PET2	0.24712716
PLT_PRE	0.27813620
HB_PRE	0.28155371
CRP_PRE	0.28847137
SLR_pre	0.28879571
BW_PRE	0.35026204
AGE	0.36209905
LDH_PRE	0.36253452
eGFR_PRE	0.47128317
ESTIMATED_SPLEE_VOL_PRE_PET1	0.47132205
WBC_PRE	0.58493396
BGL_PRE_PET1	0.59556093
RBC_PRE	0.68061139
BMLR_pre	0.69811238
BM_UPT_PRE_PET1	0.72756794
TIME_BETWEE_PET	0.73894218
AST_PRE	0.83692878
SPLEEN_FDG_UPT_PRE_PET1	0.86169699
ALT_PRE	0.91175656
K_PRE	0.94949537
ALBUMIN_PRE	0.96210768

Anova

Ahora hacemos la ANOVA que es el equivalente paramétrico a Kurskall Wallis

## ANOVA - para todos los casos
ANOVA_p<-as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) anova(lm(x ~ datos$STATUS))$`Pr(>F)` [1])))) %>%
  arrange(V1)
                                              
ANOVA_p

A data.frame: 25 × 1

	V1
	<dbl>
LIVER_PRE_PET1	0.03959446
BMI	0.13130022
eGFR_PRE	0.16275397
BW_PRE	0.19551371
TIME_BETWEE_PET	0.20064551
ALT_PRE	0.21078341
AST_PRE	0.22687045
PLT_PRE	0.25324994
AGE	0.31995232
LDH_PRE	0.34219043
HB_PRE	0.35305717
CRP_PRE	0.38557634
OVERALL_TIME	0.38727919
dias	0.43112427
BGL_PRE_PET1	0.43388029
SLR_pre	0.55708977
BMLR_pre	0.62489716
RBC_PRE	0.65604217
CYCLES_BETWEEN_PET1_PET2	0.68048920
ESTIMATED_SPLEE_VOL_PRE_PET1	0.71612406
WBC_PRE	0.73485464
BM_UPT_PRE_PET1	0.79115621
K_PRE	0.79941543
SPLEEN_FDG_UPT_PRE_PET1	0.80844654
ALBUMIN_PRE	0.81478133

Feature selection univariado: cualitativa vs la salida

Chi cuadrado de Pearson

res_sig <- rep(0,length(var_cual))

for (i in 1:length(var_cual)){
  chisq <- chisq.test(table(as.factor(as.character(datos[,var_cual[i]])),datos[,'STATUS']),correct = TRUE,simulate.p.value = 10000)
  res_sig[i]<-chisq$p.value
}
df_sig <- data.frame(p_valor=res_sig)
row.names(df_sig) <- colnames(datos[,var_cual])

idx_sig <- sort(df_sig$p_valor,decreasing = FALSE,index.return=TRUE)$ix
df_sig_sort <- data.frame(p_valor=res_sig[idx_sig])
row.names(df_sig_sort) <- colnames(datos[,var_cual])[idx_sig]
df_sig_sort

A data.frame: 8 × 1

	p_valor
	<dbl>
ACTION_TAKEN_	0.01399300
TNM_STAGE	0.02348826
DIAGNOSTIC	0.14492754
ECOGPS	0.18440780
GENDER	0.25187406
COMORBIDITIES	0.31534233
CTCNCI	0.67266367
TREATMENT	1.00000000

Filtro univariado Mutual information (MI)

# Mutual informacion variables cuantitativas:
inf_gain <- information.gain(STATUS~., data = datos)
inf_gain <- inf_gain %>%
  arrange(desc(attr_importance))
inf_gain

A data.frame: 33 × 1

	attr_importance
	<dbl>
ACTION_TAKEN_	1.327590e-01
DIAGNOSTIC	1.265327e-01
TNM_STAGE	8.123244e-02
ECOGPS	5.316641e-02
COMORBIDITIES	5.272323e-02
CTCNCI	2.358443e-02
GENDER	1.294311e-02
TREATMENT	7.687645e-05
CYCLES_BETWEEN_PET1_PET2	0.000000e+00
AGE	0.000000e+00
WBC_PRE	0.000000e+00
RBC_PRE	0.000000e+00
HB_PRE	0.000000e+00
PLT_PRE	0.000000e+00
CRP_PRE	0.000000e+00
ALBUMIN_PRE	0.000000e+00
LDH_PRE	0.000000e+00
eGFR_PRE	0.000000e+00
AST_PRE	0.000000e+00
ALT_PRE	0.000000e+00
K_PRE	0.000000e+00
BGL_PRE_PET1	0.000000e+00
BMI	0.000000e+00
BW_PRE	0.000000e+00
SPLEEN_FDG_UPT_PRE_PET1	0.000000e+00
BM_UPT_PRE_PET1	0.000000e+00
LIVER_PRE_PET1	0.000000e+00
ESTIMATED_SPLEE_VOL_PRE_PET1	0.000000e+00
TIME_BETWEE_PET	0.000000e+00
dias	0.000000e+00
SLR_pre	0.000000e+00
BMLR_pre	0.000000e+00
OVERALL_TIME	0.000000e+00

Feature selection multivariado - BIC modelo estadístico - BINARIO

# como elegia muchas variables fallaba(necesita menos variables),por eso ponemos a mano las que queremos(se puede jugar intercambiando variables)
# calcular los 5 modelos segun el BIC
modelos_5top <-
  glmulti(STATUS~ACTION_TAKEN_+TNM_STAGE+DIAGNOSTIC+LIVER_PRE_PET1+dias+OVERALL_TIME+BMI+CYCLES_BETWEEN_PET1_PET2+SPLEEN_FDG_UPT_PRE_PET1+BM_UPT_PRE_PET1+ESTIMATED_SPLEE_VOL_PRE_PET1+SLR_pre+BMLR_pre, data = datos,
          level = 1,               # No interaction considered
          method = "h",            # Exhaustive approach
          crit = "bic",            # BIC as criteria
          confsetsize = 5,         # Keep 5 best models
          plotty = F, report = F,  # No plot or interim reports
          fitfunction = "glm",     # glm function
          family = binomial)       # binomial family for logistic regression

# Los 5 mejores modelos
print(modelos_5top@formulas)
options(warn = -1)

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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message in glmulti(y = "STATUS", data = datos, level = 1, method = "h", :
"!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA"
Warning message in glmulti(y = "STATUS", data = datos, level = 1, method = "h", :
"!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA"

[[1]]
STATUS ~ 1 + LIVER_PRE_PET1 + BMI
<environment: 0x0000024ae70aa830>

[[2]]
STATUS ~ 1 + LIVER_PRE_PET1
<environment: 0x0000024ae70aa830>

[[3]]
STATUS ~ 1 + LIVER_PRE_PET1 + dias + BMI
<environment: 0x0000024ae70aa830>

[[4]]
STATUS ~ 1
<environment: 0x0000024ae70aa830>

[[5]]
STATUS ~ 1 + TNM_STAGE + LIVER_PRE_PET1 + BMI
<environment: 0x0000024ae70aa830>

# El summary de los 5 mejores modelos
summary(modelos_5top@objects[[5]])

Call:
fitfunc(formula = as.formula(x), family = ..1, data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7525  -0.6763  -0.3955   0.7997   2.3384  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)   
(Intercept)      0.6515     2.6802   0.243  0.80794   
TNM_STAGEII     -2.2655     1.2897  -1.757  0.07899 . 
TNM_STAGEIII    -2.7072     1.2252  -2.210  0.02714 * 
TNM_STAGEIV     -3.6752     1.4965  -2.456  0.01405 * 
LIVER_PRE_PET1   2.4072     0.9164   2.627  0.00862 **
BMI             -0.2317     0.1080  -2.145  0.03196 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 76.823  on 58  degrees of freedom
Residual deviance: 56.599  on 53  degrees of freedom
AIC: 68.599

Number of Fisher Scoring iterations: 5

# Las variables que aparecen en el mejor modelo son:
var_sel_mod<-names(modelos_5top@objects[[5]]$coefficients)[-1]
print(var_sel_mod)

var_sel_mod1<-c("TNM_STAGE","LIVER_PRE_PET1","BMI")

[1] "TNM_STAGEII"    "TNM_STAGEIII"   "TNM_STAGEIV"    "LIVER_PRE_PET1"
[5] "BMI"           

# Calculamos el mejor modelo y su accuracy:

# Construimos la formula de forma automatica:
x = paste(var_sel_mod1, collapse = " + ")
y = respuesta
formBIC = as.formula(paste(y, "~", x))
formBIC

STATUS ~ TNM_STAGE + LIVER_PRE_PET1 + BMI

modelo1 <- train(formBIC, data=datos,method='glm')

    print("Matriz de confusión")
    pred <- predict(newdata=datos,modelo1) # prediccion usando el modelo logistico
    real <- as.factor(datos[,respuesta]) # respuesta real
    cm_train_TOT <- caret::confusionMatrix(data=pred,reference=real) # guardamos la matriz de confusion de training
    print(cm_train_TOT) # pintamos la matriz de confusion de training

[1] "Matriz de confusión"
Confusion Matrix and Statistics

          Reference
Prediction ALIVE DEATH
     ALIVE    33     9
     DEATH     5    12
                                          
               Accuracy : 0.7627          
                 95% CI : (0.6341, 0.8638)
    No Information Rate : 0.6441          
    P-Value [Acc > NIR] : 0.03564         
                                          
                  Kappa : 0.4594          
                                          
 Mcnemar's Test P-Value : 0.42268         
                                          
            Sensitivity : 0.8684          
            Specificity : 0.5714          
         Pos Pred Value : 0.7857          
         Neg Pred Value : 0.7059          
             Prevalence : 0.6441          
         Detection Rate : 0.5593          
   Detection Prevalence : 0.7119          
      Balanced Accuracy : 0.7199          
                                          
       'Positive' Class : ALIVE           
                                          

Feature selection Boruta

# Voy a definir el data frame de entrada: X
X = datos[-1]
head(X)

# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)

A data.frame: 6 × 33

	CYCLES_BETWEEN_PET1_PET2	GENDER	AGE	TNM_STAGE	WBC_PRE	RBC_PRE	HB_PRE	PLT_PRE	CRP_PRE	ALBUMIN_PRE	LDH_PRE	eGFR_PRE	AST_PRE	ALT_PRE	K_PRE	BGL_PRE_PET1	BMI	BW_PRE	SPLEEN_FDG_UPT_PRE_PET1	BM_UPT_PRE_PET1	LIVER_PRE_PET1	ESTIMATED_SPLEE_VOL_PRE_PET1	DIAGNOSTIC	TREATMENT	ECOGPS	COMORBIDITIES	CTCNCI	ACTION_TAKEN_	TIME_BETWEE_PET	dias	SLR_pre	BMLR_pre	OVERALL_TIME
	<dbl>	<fct>	<dbl>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<fct>	<fct>	<fct>	<fct>	<fct>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	3	MALE	54	III	3.82	3.03	9.70	146	3.67	2.13	573.0	60.1	16.8	6.7	4.87	93	22.90	66.5	2.67	2.86	2.45	316.5	GINECOLOGICAL	ICI	SYMPTOMATIC BUT AMBULATORY	HYPERLIPIDEMIA	SEVERE	DRUG INTERRUPTED	2	25	0.8586957	1.0144928	2
2	2	FEMALE	79	IV	10.87	3.93	11.50	515	4.11	2.65	158.0	57.3	13.5	8.0	3.92	108	16.47	33.5	2.40	2.70	2.70	102.3	HEAD AND NECK	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DRUG INTERRUPTED	4	0	1.0099502	0.9303483	50
3	3	MALE	60	III	8.40	4.41	8.56	355	0.09	4.32	77.8	82.1	19.1	13.7	4.56	87	20.15	58.1	2.57	1.31	2.27	81.5	LUNG CANCER	ICI	SYMPTOMATIC,<50% IN BED DURING THE DAY	DIABETES MELLITUS	MODERATE	DOSE REDUCED	4	14	0.9955752	1.0884956	2
4	3	MALE	76	II	4.66	3.55	11.70	178	0.10	3.70	195.0	65.7	17.7	15.3	3.83	135	22.83	56.7	2.25	2.46	2.26	224.4	RENAL CANCER	ICI	ASYMTOMATIC	HYPERTENSION	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	0.8811475	0.7172131	12
5	5	FEMALE	70	II	5.86	5.19	15.10	343	7.26	3.94	166.0	79.9	18.1	10.2	3.83	111	18.29	41.7	2.48	2.52	2.55	260.0	MELANOMA	ICI	SYMPTOMATIC BUT AMBULATORY	DIABETES MELLITUS	NO SIDE EFFECTS	DOSE NOT CHANGED	6	0	0.8269231	1.0192308	12
6	4	MALE	54	IV	2.95	4.45	14.50	167	0.25	4.16	135.0	76.3	14.4	14.0	4.55	98	27.18	80.4	2.01	1.40	2.83	198.4	LUNG CANCER	ICI	ASYMTOMATIC	CANCER RRECURRENCE	HIGHT SEVERE	DOSE NOT CHANGED	6	363	1.1321586	0.5770925	18

1. ALIVE
2. ALIVE
3. ALIVE
4. ALIVE
5. ALIVE
6. ALIVE

Levels:

1. 'ALIVE'
2. 'DEATH'

# Determinar la importancia de los atributos.
boruta.model <- Boruta(y~., data = cbind(X,y), doTrace = 2)

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After 12 iterations, +0.32 secs: 

 rejected 28 attributes: ALBUMIN_PRE, ALT_PRE, AST_PRE, BGL_PRE_PET1, BM_UPT_PRE_PET1 and 23 more;

 still have 5 attributes left.


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After 16 iterations, +0.39 secs: 

 rejected 1 attribute: ECOGPS;

 still have 4 attributes left.


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After 35 iterations, +0.69 secs: 

 rejected 1 attribute: eGFR_PRE;

 still have 3 attributes left.


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After 65 iterations, +1.2 secs: 

 confirmed 1 attribute: TNM_STAGE;

 still have 2 attributes left.


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options(repr.plot.width=12, repr.plot.height=8)

print(boruta.model)
plot(boruta.model)

Boruta performed 99 iterations in 1.677527 secs.
 1 attributes confirmed important: TNM_STAGE;
 30 attributes confirmed unimportant: ALBUMIN_PRE, ALT_PRE, AST_PRE,
BGL_PRE_PET1, BM_UPT_PRE_PET1 and 25 more;
 2 tentative attributes left: ACTION_TAKEN_, AGE;

# Refinar modelo para resolver posibles atributos tentativos.
# Dado que pueden existir atributos no resueltos (tentativos), refinamos el modelo.

boruta.model2 <- TentativeRoughFix(boruta.model)
print(boruta.model2)
plot(boruta.model2)

Boruta performed 99 iterations in 1.677527 secs.
Tentatives roughfixed over the last 99 iterations.
 1 attributes confirmed important: TNM_STAGE;
 32 attributes confirmed unimportant: ACTION_TAKEN_, AGE, ALBUMIN_PRE,
ALT_PRE, AST_PRE and 27 more;

# Obtener una lista de los atributos y sus etiquetas con el analisis de Boruta
as.data.frame(boruta.model$finalDecision)

A data.frame: 33 × 1

	boruta.model$finalDecision
	<fct>
CYCLES_BETWEEN_PET1_PET2	Rejected
GENDER	Rejected
AGE	Tentative
TNM_STAGE	Confirmed
WBC_PRE	Rejected
RBC_PRE	Rejected
HB_PRE	Rejected
PLT_PRE	Rejected
CRP_PRE	Rejected
ALBUMIN_PRE	Rejected
LDH_PRE	Rejected
eGFR_PRE	Rejected
AST_PRE	Rejected
ALT_PRE	Rejected
K_PRE	Rejected
BGL_PRE_PET1	Rejected
BMI	Rejected
BW_PRE	Rejected
SPLEEN_FDG_UPT_PRE_PET1	Rejected
BM_UPT_PRE_PET1	Rejected
LIVER_PRE_PET1	Rejected
ESTIMATED_SPLEE_VOL_PRE_PET1	Rejected
DIAGNOSTIC	Rejected
TREATMENT	Rejected
ECOGPS	Rejected
COMORBIDITIES	Rejected
CTCNCI	Rejected
ACTION_TAKEN_	Tentative
TIME_BETWEE_PET	Rejected
dias	Rejected
SLR_pre	Rejected
BMLR_pre	Rejected
OVERALL_TIME	Rejected

# Obtener una lista de los atributos importantes y tentativos
opc_boruta = getSelectedAttributes(boruta.model2, withTentative = F)
opc_boruta

'TNM_STAGE'

# Construimos la formula de forma automatica:
x = paste(opc_boruta, collapse = " + ")
y = respuesta
formBoruta = as.formula(paste(y, "~", x))
formBoruta

STATUS ~ TNM_STAGE

Curva Roc

library(ROCR)

index = sample(1:nrow(datos), size = .100 * nrow(datos))
#index
train = datos[index, ]
test = datos[-index, ]

model = glm(STATUS~TNM_STAGE+ACTION_TAKEN_+DIAGNOSTIC+LIVER_PRE_PET1+dias+BMI+CYCLES_BETWEEN_PET1_PET2,data=train, 
            family = binomial(link = "logit"))

pred = predict(model,test,type="response")
pred = prediction(pred,test$STATUS)
perf = performance(pred, "acc")
#plot(perf)

max_ind = which.max(slot(perf, "y.values")[[1]] )
acc = slot(perf, "y.values")[[1]][max_ind]
cutoff = slot(perf, "x.values")[[1]][max_ind]
print(c(accuracy= acc))

perf_cost = performance(pred, "cost")
perf_err = performance(pred, "err")
perf_tpr = performance(pred, "tpr")
perf_sn_sp = performance(pred, "sens", "spec")

roc = performance(pred,"tpr","fpr")
plot(roc, colorize = T, lwd = 2)
abline(a = 0, b = 1)

auc = performance(pred, measure = "auc")
print(auc@y.values)

 accuracy 
0.7407407 
[[1]]
[1] 0.7661765

CONCLUSIONES

Aqui nos pasa lo mismo que con el modelo con los datos de DIFF.
Los graficos es lo que mas nos puede aportar como conclusiones mas intesantes.